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Multimedia Watermarking using Intelligent
Techniques
Khurram Jawad
Doctor of Philosophy
2015
Pakistan Institute of Engineering & Applied Sciences
Multimedia Watermarking Using Intelligent Techniques
Page ii
Multimedia Watermarking using Intelligent
Techniques
Khurram Jawad
Submitted in partial fulfillment of the requirements
for the degree of Ph.D
2015
Department of Computer and Information Sciences,
Pakistan Institute of Engineering and Applied Sciences,
Nilore, Islamabad
Multimedia Watermarking Using Intelligent Techniques
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In the Name of Allah, the Most Beneficent, the Most Merciful
Multimedia Watermarking Using Intelligent Techniques
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This thesis is prepared under the supervision of
Dr. Asifullah Khan
Associate Professor
Department of Computer and Information Sciences,
Pakistan Institute of Engineering and Applied Sciences,
Islamabad, Pakistan
Financial support by Higher Education Commission Pakistan
through indeginous-5000 PhD fellowship program Batch-VI,
Grant No. 074-0773-Ps4-403.
Multimedia Watermarking using Intelligent Techniques
Multimedia Watermarking Using Intelligent Techniques
Page v
Declaration of Originality
I hereby declare that the work contained in this thesis and the intellectual content of this thesis
are the product of my own work. This thesis has not been previously published in any form nor
does it contain any verbatim of the published resources which could be treated as infringement of
the international copyright law. I also declare that I do understand the terms „copyright‟ and
„plagiarism,‟ and that in case of any copyright violation or plagiarism found in this work, I will
be held fully responsible of the consequences of any such violation.
Signature: _____________________
Name: ___ KhurramJawad ____
Date: _____________________
Place: _____________________
Multimedia Watermarking Using Intelligent Techniques
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Certificate
This is to certify that the work contained in this thesis entitled: Multimedia Watermarking
Using Intelligent Techniques, was carried out by: Khurram Jawad, and in my opinion, it is
fully adequate, in scope and quality, for the degree of Ph.D.
Supervisor:……………………………
(Dr. Asifullah Khan)
Head, Department Name ……………………
(Dr. Javaid Khurshid)
Multimedia Watermarking Using Intelligent Techniques
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Copyright Statement
The entire contents of this thesis titled „Multimedia Watermarking Using Intelligent
Techniques‟ and authored by Mr. Khurram Jawad, are an intellectual property of Pakistan
Institute of Engineering & Applied Sciences (PIEAS). No portion of the thesis should be
reproduced without obtaining explicit permission from PIEAS.
Multimedia Watermarking Using Intelligent Techniques
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Dedicated to my beloved Parents
Multimedia Watermarking Using Intelligent Techniques
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LIST OF PUBLICATIONS
1. K. Jawad and A. Khan, "Genetic algorithm and difference expansion based reversible
watermarking for relational databases," Journal of Systems and Software, vol. 86, pp. 2742-
2753, 2013.
2. S. A. Malik, A. Khan, M. Hussain, K. Jawad, R. Chamlawi, and A. Jalil, "Authentication of
images for 3D cameras: Reversibly embedding information using intelligent approaches,"
Journal of Systems and Software, vol. 85, pp. 2665-2673, 2012.
3. K. Jawad and A. Khan, “Robust and Blind Watermarking of Relational Databases Using Reversible
Contrast Mapping”. To be submitted in, IEEE Transactions on information forensics and security.
4. I. Hafeez, K. Jawad, M. Chaumont, and A. Khan, “Watermarking of DNA Sequences: Hybrid
Synonymous Substitution of Nucleotides and Dual Layer Error Correction”. Submitted in Protein and
Peptide Letters.
Multimedia Watermarking Using Intelligent Techniques
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TABLE OF CONTENTS
Multimedia Watermarking using Intelligent Techniques ................................................................ i
Declaration of Originality ............................................................................................................... v
Acknowledgement ...................................................................................................................... xvii
List of Publications ........................................................................................................................ ix
Table of Contents ............................................................................................................................ x
List of Figures ............................................................................................................................... xii
List of Tables ............................................................................................................................... xiv
Abstract ......................................................................................................................................... xv
Abbreviations/Key Words ........................................................................................................... xvi
Introduction ............................................................................................................... 1 Chapter 1
1.1 Motivation and Objectives ............................................................................................... 3
1.2 Research Perspective ........................................................................................................ 4
1.3 Thesis Structure ................................................................................................................ 4
1.4 Contribution ..................................................................................................................... 5
Literature Review...................................................................................................... 7 Chapter 2
2.1 Genetic Algorithm and Difference Expansion based Watermarking for Databases ........ 7
2.2 Reversible and Blind Watermarking Technique for Relational Databases ...................... 9
2.3 Synonymous Substitution based Watermarking for DNA Sequences ........................... 10
2.4 Chapter Summary ........................................................................................................... 12
GA and DEW based Watermarking for Databases ................................................. 13 Chapter 3
3.1 Reversible Difference Expansion Watermarking (DEW) Method ................................ 14
3.2 Genetic Algorithm based Difference Expansion Watermarking (GADEW) Method .... 15
3.2.1 Message Authentication Code (MAC).................................................................... 16
3.2.2 Chromosome Structure of the Genetic Algorithm (GA) ......................................... 17
3.2.3 Calculating Fitness .................................................................................................. 18
3.2.4 Example of Obtaining TC, CrC, AwD, and TwD................................................... 21
3.2.5 Watermark Embedding ........................................................................................... 23
3.2.6 Watermark Extraction ............................................................................................. 25
3.3 Results and Analysis ...................................................................................................... 25
3.3.1 Capacity Analysis ................................................................................................... 26
3.3.2 Security Analysis .................................................................................................... 28
3.3.3 Different Attacks ..................................................................................................... 32
3.4 Chapter Summary ........................................................................................................... 37
Reversible and Blind Watermarking for Databases ................................................ 38 Chapter 4
4.1 Proposed Reversible and Blind Watermarking Technique for Relational Database ..... 39
4.1.1 Automatic Bit Checking ......................................................................................... 39
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4.1.2 RCM Transform ...................................................................................................... 40
4.1.3 Distortion Tolerance (DT) Check ........................................................................... 41
4.1.4 Watermark Embedding ........................................................................................... 42
4.1.5 Watermark Extraction ............................................................................................. 45
4.1.6 Analyzing Three Steps of RBW-RD ....................................................................... 47
4.1.7 Reduction in Watermarking Distortion ................................................................... 48
4.2 Improvements of The Proposed RBW-RD Technique Over RCM Technique .............. 49
4.2.1 Increased Watermarking Capacity .......................................................................... 49
4.2.2 Less Distortion with Same Capacity ....................................................................... 50
4.2.3 Reducing FPs and Distortion Because of Addition and Bit Flipping Attack ......... 53
4.3 Robustness Analysis of The Proposed RBW-RD Method ............................................. 55
4.4 Comparison of Proposed RBW-RD Technique with DEW Technique ......................... 59
4.4.1 Experimental Analysis of The Proposed RBW-RD Technique against DEW
Technique .............................................................................................................................. 60
4.5 Chapter Summary ........................................................................................................... 63
Watermarking of DNA Sequences .......................................................................... 65 Chapter 5
5.1 Sequences Used for Testing ........................................................................................... 66
5.2 The Proposed SSW-DNA Method ................................................................................. 66
5.2.1 Data Embedding Section......................................................................................... 67
5.2.2 Correction of Errors ................................................................................................ 68
5.2.3 Employing RS Codes for Restoring Mutation Losses ............................................ 68
5.2.4 Enhanced Synonymous Substitution Technique ..................................................... 70
5.2.5 Data Extraction Section .......................................................................................... 74
5.3 Results and Analysis ...................................................................................................... 78
5.3.1 Capacity of Storing Bits .......................................................................................... 78
5.3.2 RS Codes for Error Correction ............................................................................... 79
5.4 Comparison with Existing Methods ............................................................................... 82
5.5 Chapter Summary ........................................................................................................... 86
CONCLUSIONS AND FUTURE DIRECTIONS.................................................. 87 Chapter 6
6.1 Thesis Summary ............................................................................................................. 87
6.2 Future Research Directions ............................................................................................ 89
6.2.1 Intelligent Watermarking ........................................................................................ 89
6.2.2 Reversible Watermarking ....................................................................................... 89
6.2.3 Watermarking Different Objects ............................................................................. 90
References ..................................................................................................................................... 91
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LIST OF FIGURES
Figure 1.1 Watermarking System. .................................................................................................. 2
Figure 3.1 Chromosome Structure of GA ..................................................................................... 17
Figure 3.2 Calculating Attribute Wise Distortion ......................................................................... 20
Figure 3.3 An Example Of Calculating TC, Awd, Twd, And Crc ............................................... 23
Figure 3.4 Process Of Watermark Embedding ............................................................................. 24
Figure 3.5 Process Of Watermark Detection ................................................................................ 25
Figure 3.6 Capacity Comparison Of GADEW And DEW Method Using R-Dataset .................. 27
Figure 3.7 Capacity Comparison Of GADEW And DEW Method Using FCT-Dataset .............. 28
Figure 3.8 Std Comparison Of Ord, DEW, And GADEW Method Using R-Database ............... 30
Figure 3.9 GADEW method Bit Flipping, Deletion, And Addition Attack Comparison ............. 33
Figure 3.10 Tuple-Wise-Multifaceted Attacks Comparison Between DEW And GADEW
Method .......................................................................................................................................... 35
Figure 3.11 Attribute-Wise-Multifaceted Attacks Comparison between DEW and GADEW
Method .......................................................................................................................................... 36
Figure 4.1 RCM Domain For 8-Bit Attribute ............................................................................... 40
Figure 4.2 Block diagram Of Watermark Embedding Phase. ...................................................... 43
Figure 4.3 Block Diagram Of Watermark Extraction Phase. ........................................................ 44
Figure 4.4 Embedding and Extraction Algorithms of The Proposed RBW-RD Technique ......... 46
Figure 4.5 Capacity Comparisons After Deletion Attack ............................................................. 50
Figure 4.6 Measuring Capacity Against Varying DT ................................................................... 52
Figure 4.7 Watermark Detection After Bit Flipping Attack (With BitCheck) ............................. 53
Figure 4.8 Watermark Detection After Addition Attack (With And Without Bitcheck) ............. 54
Figure 4.9 Comparisons Of FPs Between Bit Flipping And Addition Attack .............................. 55
Figure 4.10 FP&TP Detection After Bit Flipping Attack(Without BitCheck) .............................. 56
Figure 4.11 RBW-RD Capacity Comparison Of Simple Relation And After 10% Subtraction,
Addition, & Bit Flipping Attacked................................................................................................ 56
Figure 4.12 RBW-RD Capacity Comparison After 10% To 80% Bit Flipping Attack ................ 57
Figure 4.13 RBW-RD Capacity Comparison of Simple Relation And After 80% Subtraction,
Addition, & Bit flipping (50% Attributes Altered) ....................................................................... 58
Figure 4.14 RBW-RD Capacity Comparison Of Simple Relation And After 80% Subtraction,
Addition, & Bit Flipping Attacked................................................................................................ 58
Figure 4.15 DEW Method Comparison After Subtraction, Bit Flipping, Addition Attack, & No
Attack On Relation (Average Of 10, 20…90% Attack) ............................................................... 59
Figure 4.16 RBW-RD And DEW Method Comparison After Bit Flipping Attack (After Taking
Average Of 0.01, 0.02, 0.04, 0.08, And 0.17)............................................................................... 60
Figure 4.17 RBW-RD Comparison After Subtraction, RBF, Addition Attack, & No Attack On
Relation (Average of 10, 20…90% Attack) ................................................................................. 61
Figure 4.18 RBW-RD And DEW Method Comparison After Addition Attack (After Taking
Average Of 0.01, 0.02, 0.04, 0.08, And 0.17) ............................................................................... 61
Figure 4.19 Capacity Of DEW Method After Changing Values Of DT ....................................... 62
Multimedia Watermarking Using Intelligent Techniques
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Figure 4.20 Decrease In Distortion By Using RBW-RD (Fixed DT) Over DEW (Changing DT).
....................................................................................................................................................... 63
Figure 5.1 SSW-DNA Method ..................................................................................................... 67
Figure 5.2 RS Code Implementation ............................................................................................ 69
Figure 5.3 Structure of Text Encoded Using RS Coder................................................................ 69
Figure 5.4 Synonymous Substitution ............................................................................................ 70
Figure 5.5 Data Insertion In 4-Fold Degenerative Codons ........................................................... 72
Figure 5.6 Data Insertion In 2-Fold Degenerative Codons ........................................................... 73
Figure 5.7 Data Embedding Module ............................................................................................. 74
Figure 5.8 Reconstruction Of DNA Using Binary Strings ........................................................... 76
Figure 5.9 Data Extraction Module .............................................................................................. 77
Figure 5.10 RS Coder Performance For Point And Burst Mutation Scenario .............................. 80
Figure 5.11 RS Coder Performance In Burst Mutation Scenario ................................................. 81
Figure 5.12 RS Coder Performance In Point Mutation Scenario.................................................. 82
Figure 5.13 Bpn Comparison ........................................................................................................ 83
Figure 5.14 Average Uncorrected Mutations At Different Block Sizes ....................................... 84
Figure 5.15 Average Uncorrected Mutation Trend At Different N/K .......................................... 85
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LIST OF TABLES
Table 3.1: Results Of Reducing Tuple And Attribute-Wise Distortion ....................................... 31 Table 3.2: Results Of Modification In Std And Mean .................................................................. 32 Table 4.1: Probability Of Watermarking For All Three Steps ...................................................... 48 Table 4.2: Mean And Std (Distortion) By Varying DT For Three Watermarked Relations ........ 51 Table 4.3: Measuring Improvement In Distortion By Using Different DT .................................. 52 Table 4.4: Effect Of Bit Checking On Distortion, Because Of FP‟s Caused By Bit Flipping And
Addition Attack (DT Check = 0-250) ........................................................................................... 54 Table 5.1: Dataset ......................................................................................................................... 66 Table 5.2: Data Encoding Table ................................................................................................... 68 Table 5.3: Synonymous Substitution ............................................................................................ 71 Table 5.4: VOSS Representation Of DNA Sequence ................................................................... 75 Table 5.5: Bit Storage Capacity .................................................................................................... 79 Table 5.6: Comparison With Existing Techniques For Data Hiding Capacity ............................. 83
Multimedia Watermarking Using Intelligent Techniques
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ABSTRACT
Increase in research and commercial interest is observed in the area of digital watermarking in current
era. Main reason behind this development is the extensive use of internet with high speed and bandwidth
availability. As a result, sharing of multimedia content is increased significantly, which rises need for
copyright protection and authentication. Digital watermarking provides vast commercial applications for
securing content of different multimedia objects. However, watermarking can cause permanent loss in the
content of the object. Different sensitive multimedia objects like medical, military, scientific etc. do not
tolerate such permanent loss. Therefore, the distortion needs to be minimized or completely eliminated so
that the true purpose of the multimedia content can be retained. Numerous watermarking techniques are
available for different sensitive applications. Our objective is to propose reversible and secure techniques
for watermarking different multimedia objects. Robust watermarking approaches are targeted in this
research. Main focus is to achieve maximum watermarking capacity and to minimize embedding
distortion, while preserving functional capability of the underlying multimedia object.
Two different types of multimedia objects are targeted in our dissertation; these include relational
databases and DNA medium. Genetic algorithm is selected in order to apply intelligent techniques for
improving different properties of digital watermarking. Two relational database watermarking approaches
are designed and developed; both approaches are reversible, robust, and follow the distortion tolerance of
the attributes. First approach uses genetic algorithm to improve capacity, reduce distortion, and false
positive rate of the difference expansion based watermarking technique. Whereas, second approach
improves watermarking capacity, reduces distortion, and false positive rate of the reversible contrast
mapping, which is first time designed for relational databases.
A robust data hiding technique for DNA medium is proposed, which increases watermark capacity by
improving current synonymous substitution technique and resists mutation losses. Moreover, synonymous
substitution does not causes any disturbance in the amino acid sequence, thus the DNA functionality is
retained. In order to tackle different DNA mutations binary strings and Reed Solomon Codes are applied.
The watermark is encoded before embedding using Reed Solomon Codes and the structural information is
retained using binary strings.
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ABBREVIATIONS/KEY WORDS Abbreviation Description Abbreviation Description
RS Reed Solomon bs Bit stored
FP False positive A, C Adenine and Cytosine
TP True positive T, G Thiamine and Guanine
RCM Reversible Contrast Mapping bpn Bit Per Nucleotide
DT Distortion Tolerance DNA Deoxyribonucleic Acid
Std Standard Deviation RS Reed-Solomon
Λ Watermark embedding/extracting
parameter
n final watermark data (party and data
bits combined) embedded in DNA
R Original Relation k Data bits to be embedded in DNA
GA Genetic Algorithm n-k Number of parity bits
LSB Least Significant Bit. GMO Genetically Modified Organisms
DE Differential Evolution CrC Capacity related Cost
RR Restored Relation TwD Tuple-wise Distortion
H MAC Hashing Function AwD Attribute-wise Distortion
SK Secret Key TC Total cost
PK Primary Key OrD Original database
ti.PK Primary key of the Tuple i T Word length of attribute
1/ λ Fraction of tuple selected L Upper limit of RCM Domain
R-dataset Random Dataset Dec Binary to decimal
FCT-dataset Forest Cover Type Dataset RW Watermarked Relation
TV Target Value a Mark-able attributes
CV Changed Value M_OrD Attribute wise mean of OrD
TC Total Cost b Automatically generated watermark bit
Int_x, Int_y Integer value of attribute x and y M_DEW Attribute wise mean of RW using DEW
BCH Bose-Chaudhuri- Hocquenghem
Algorithm
S_GADEW Attribute wise Std of RW using
GADEW
Int_x', Int_y' Watermarked Integer value of
attribute x' and y'
S_DEW Attribute wise Std of RW using DEW
Frac_x, Frac_y Fraction value of attribute x and y S_OrD Attribute wise Std of R
Frac_x', Frac_y' Watermarked fraction value of
attribute x' & y'
DA, DB, & DC Distortion (mean & Std) measure for
RW using different value of DT
H MAC Hashing function DO Distortion measure for R
M_GADEW Attribute wise mean of watermarked
database using GADEW
DE, DF Distortion measure for RR with and
without bit checking
DEW Difference Expansion Based
Watermarking
δjk Upper (δj1) and lower(δj2) limit of DT
for attribute j
UCa Number of potential codons for
watermarking
UnCa Number of restricted codons for
watermarking
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ACKNOWLEDGEMENT
All praises to Almighty ALLAH, creator of the Universe, Who made us the super creature, blessed us
with knowledge. I am grateful to Almighty ALLAH, the most Benevolent and Merciful, Who blessed me
throughout my life and gave me the ability to undertake such a challenging task and proceeding towards
completion.
I extend my sincerest thanks to my supervisor, Dr. Asifullah Khan for his generous guidance and
moral support during my PhD. I appreciate his endless patience, positive attitude, ability to provide
assistance and especially his willingness to put his students before his work. I thank him greatly for his
meticulous proof reading of all of my published work. His valuable suggestions and persuasive criticism
has led me to complete my goal successfully.
A very special note of thanks goes to my Sisters, Nephew, Niece, Uncle Arshad, Uncle Irfan Saeed,
Arshad Bahi, Ehtasham, Nana, Nano, late Dada, and late Dado whose heart felt prayers, appreciation, and
support have always been a valuable asset and a great source of inspiration for me. They always
encouraged me, whenever I was demoralized during my academic career. They really deserve special
thanks for enduring all my problems with great patience and love.
I am also indebted to Dr. Abdul Jalil, Dr. Muttawarra Hussain, Dr. Javed Khurshid and all other
teachers of the department for their corporation and encouragement to attain my goal. Thanks are due to
as this work would not have been possible without their encouragement and moral support. I gratefully
acknowledge Higher Education Commission of Pakistan for the financial support provided through
Indigenous PhD scholarship program.
Last, but certainly not the least, I would like to thank my friends and colleagues (Muhammad Sami,
Muhammad Shukaib, Dr. Zia Ur Rehman, Dr. Muhammad Tahir, Adnan Idrees, Dr. Aksam Iftekhar, Dr.
Maqsood Hayat, Mehdi Hassan, Dr. Sana Ambreen, Naeem Ur Rehman, Iqbal Murtza, Ibbad Hafeez,
Faheem Khan, and Raheel Zia). They helped me in times of troubles, praised me on my achievements,
and cheered me when I was down.
Khurram Jawad
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INTRODUCTION Chapter 1
Conventionally, corporation stamps and copyright logos were used to verify ownership and
authenticity. Such procedures are safe, provided that the documents can only be transported and
copied materially. However, requirements of the digital era have changed. With the increasing use of
fast internet connections, multimedia objects are handled, altered, and transferred unlawfully over the
network. Therefore, it is no longer adequate for the objects to just have a visible logo. Digital
watermarking consists of embedding a patent mark in a multimedia content so that the proprietor can
establish his right on the multimedia object [1].
Digital watermarking is divided into three categories, robust [2-7], fragile [8-13], and semi-fragile
[14-18]. A robust watermarking system can survive both deliberate and unintended (legitimate)
attacks. Semi-fragile watermarking schemes can survive certain (unintended) alterations but the
watermark is destroyed if it undergoes any deliberate attack. Whereas, fragile watermarking technique
can detect any slight modification to the watermarked multimedia object and the watermark becomes
undetectable after the watermarked object is altered by any method [1]. The important features of a
digital watermarking system include embedding capacity, fidelity, blind or semi-blind detection,
distortion tolerance, secret and cipher keys, robustness and security, reversibility, false positive rate,
and computational time.
Improving embedding capacity means more watermark bits can be embedded, which helps to
spread the watermark all over the multimedia object. Thus, watermark can be successfully detected
even from a portion (subset) of the watermarked object [19]. High embedding capacity helps
watermark to survive different attacks even if intensity of the attacks is severe. Visual quality of the
multimedia object should not be compromised after embedding the watermark and should not affect
the true usefulness of the multimedia content [20]. In case of sensitive databases, watermarking
process can affect the usefulness of the multimedia content. Quality of the medical related databases is
important so that they can clearly help in decision making for the physicians [21]. On the other hand,
attackers can detect the perceptual change and can modify the watermarked part to remove the
Multimedia Watermarking Using Intelligent Techniques
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watermark.
Blind watermarking technique can detect a watermark in an object without requiring any side
information. Whereas, semi-blind watermarking technique requires some side information to detect the
watermark [22]. However, non-blind detection requires, original version of the watermarked object to
detect watermark. Distortion Tolerance defines the level, up to which the quality of multimedia object
can be compromised after watermarking. Distortion Tolerance or usability constraint provides the
range, up to which the distortion can be introduced into the multimedia object. This helps to keep the
distortion within the acceptable range for a particular application [23].
A general structure of a watermarking system is presented in Figure 1.1. Cipher keys can be helpful
for encrypting the watermark that is to be embedded into the multimedia object. Cypher keys are also
used for semi-blind watermarking, where the side information can be encrypted and can be transferred
to the detection side along with the watermarked object [24]. Secret keys can be used for controlling
watermark embedding and detection for the targeted multimedia object. Both secret and cipher keys
help proprietor to randomly distribute the watermark throughout the multimedia object, so that the
attacker cannot predict the location of watermark [25]. Robustness points to the ability of the
watermark to endure normal operations of the multimedia content. Whereas, security refers to the
competency of watermark to fight daunting attacks. Thus, the watermark can be extracted from the
watermarked multimedia object even after attacker modifies its content [1].
Figure 1.1 Watermarking System.
Original Object
Embedding
Watermarked Object
Recovered Object
Watermark
Watermark
Watermarked Object
Extraction
Watermark Embedder
Watermark Extractor
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Reversibility enables watermarking technique to exactly restore the original multimedia object
during watermark detection process. This property is useful for sensitive multimedia objects that can
afford zero distortion in its unwatermarked content [26]. An important application of reversibility is
that the trial version of watermarked multimedia object can be sent for testing purpose. Once the buyer
is satisfied, then he can buy full multimedia object by just obtaining the secret key for the user. Thus,
the multimedia object can be restored to its original version by using the secret key [27].
A watermark may be falsely detected in an unwatermarked region of the object. On one hand, it can
affect the process of watermark detection and on the other hand it can also degrade the quality of the
recovered object in case of reversible watermarking technique. Therefore, for a good watermarking
technique false positive rate should be minimal [22]. Computational measure of the watermarking
system provides detail about how much time a watermarking systems can take for embedding and
detection. This property is referred as computational time of the watermarking technique [28].
1.1 Motivation and Objectives
In this era, internet and associated devices are undergoing immense improvement, as a result
electronic business, electronic commerce, electronic marketing and online buying and selling have
evolved tremendously. High speed internet and related technology has made illegal file sharing and
dissemination easier [23, 29]. This requires considerable attention to the copyright protection of the
multimedia contents. Digital watermarking provides vast commercial applications for securing content
of different multimedia objects. Watermarking can cause permanent distortion into the content of the
object. However, different sensitive multimedia contents related to medical, military, scientific etc.; do
not tolerate such permanent loss.
Our objective is to construct more appropriate and operational watermarking systems for different
multimedia objects i.e. relational database and DNA medium. It should provide larger understanding
of certain watermarking properties, such as watermarking capacity, reversibility, false positive rate,
blindness, imperceptibility, distortion, and usability. Furthermore, our focus is to minimize or
completely eliminate the watermarking distortion, so that the true purpose of the multimedia content
can be retained.
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1.2 Research Perspective
Advances in internet and associated devices have raised issues of unlawful manipulation, replication,
malicious interfering, and copying of multimedia content. Therefore, need for research in multimedia
integrity and security is increasing day by day. In order to safeguard the security of the digital media,
different techniques are proposed and watermarking is regarded as powerful tool for securing the
digital content.
This work focuses on improving the embedding capacity and reducing or eliminating distortion
along with achieving reversibility and robustness in domain of relational database and DNA medium.
Reversible Contrast Mapping (RCM-DB) and Genetic Algorithm based Difference Expansion
Watermarking (GADEW) are employed for database watermarking and have shown improvements in
results of different watermarking properties, i.e. increased embedding capacity, reduced distortion,
better robustness and false positive rate. An improvement in synonymous substitution technique is
proposed for DNA watermarking, which provides high embedding capacity and better robustness.
1.3 Thesis Structure
Chapter 2 covers a brief survey of the existing watermarking approaches. It presents the ideas and
methods described by different relevant methods. To make it more clear, the literature study is divided
into three sub sections, which provide literature survey for all the three proposed techniques.
Chapter 3 presents a reversible watermarking approach for relational databases. It uses genetic
algorithm (GA) to improve capacity and reduce distortion of difference expansion (DEW) technique.
Additionally, the proposed genetic algorithm based reversible watermarking (GADEW) approach is
robust against different attacks including sorting, addition, bit flipping, deletion, additive attacks,
attribute-wise-multifaceted, and tuple-wise-multifaceted. Randomizing selection of the features also
makes it hard for the aggressor to guess watermark. Difficulty of the false positive recognition is fixed
and even addition attack does not result in false positive recognition.
Chapter 4 demonstrates a reversible and blind watermarking method for relational database (RBW-
RD), that utilizes reversible contrast mapping (RCM) to achieve reversibility. It offers distinguishing
improvement in terms of payload, while following distortion tolerance (DT) for the relation and causes
Multimedia Watermarking Using Intelligent Techniques
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less distortion and low false positives (FPs). Along with previously mentioned properties, it also
retains pre-existed properties of RCM method, that is no encryption or compression is necessary and
the computational complexity is also low. It makes the most of both fraction and integer part for
watermarking. However, even if fraction part is changed the detection process is not disturbed. Finally,
it is also compared with difference expansion (DEW) based watermarking technique.
Chapter 5 provides SSW-DNA watermarking method that attains high capacity and achieves
robustness against mutations. It exploits whole coding region as a result high data storage is attained.
Existing DNA watermarking systems use only 4-fold synonymous codons, which may not increase
watermarking capacity, as 2-fold and 3-fold synonymous codons makes substantial portion of DNA.
The proposed method facilitates the use of 4-fold, 3-fold, and 2-fold synonymous codons, enabling
high data storage capacities. Structural information is retained using binary strings that make it a semi
blind approach and watermark is encoded before embedding using Reed Solomon codes. Biologically,
synonymous substitution method maintains the amino acid sequence, thus DNA functionality is
retained.
Chapter 6 summarizes the effort accomplished in this dissertation. Furthermore, it provides certain
future directions of the effort presented in current thesis.
1.4 Contribution
This work focuses on copyright protection of digital multimedia objects, while targeting capacity,
robustness, reversibility, and imperceptibility. The research covered in this dissertation contributes in
the following areas:
A watermarking approach for relational database GADEW is proposed that is reversible, semi-
blind, and robust. It provides better results in terms of capacity, distortion and false positive
rate as compared to existing approach. Reversible database watermarking is essential for
research and business in different applications.
A reversible, blind, and robust watermarking method for relational database RBW-RD is
presented. It achieves high capacity and causes less distortion and false positives. Comparison
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with RCM and DEW based watermarking method shows that the proposed RBW-RD
approach is superior.
A new semi-blind data hiding technique for DNA medium SSW-DNA is proposed. It provides
high embedding capacity along with providing robustness against DNA mutations. The
proposed technique uses improved synonymous substitution technique, which does not, affects
functionality of DNA.
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LITERATURE REVIEW Chapter 2
A brief survey of the existing watermarking approaches in field of relation database and DNA
watermarking is provided in current chapter. The ideas and methods described by different current
researchers are elaborated. For simplicity, the literature study is divided into three separate sections
representing three techniques.
2.1 Genetic Algorithm and Difference Expansion based
Watermarking for Databases
Major reason behind growth in research and business is because of easy availability of the internet.
These days, distributing data upon internet is essential for research and business, which also
encompasses buying/selling of database. Exchange of information in some important areas like
scientific, medical, stock market etc. is essential. Therefore, it is necessary to restrict unlawful copying
and circulation of relational databases [18]. In this regard, tamper resilient shipping and proof of
proprietorship of relational databases is the utmost puzzling concern [19].
Sharing of different multimedia objects (for example image, text, and audio etc.) can be secured
using watermarking techniques [30]. Likewise, watermarking provides effective solution for securing
relational databases. Though, a relational database provides very little bandwidth for embedding
watermark into its contents. Therefore, embedding more payloads in database can result in losing true
meaning of its content. At the start, Rakesh and Jerry [31] used least significant bits (LSB) of database
attributes for embedding watermark bits. As a result, they permanently altered the relational database
for watermarking. However, the attackers can easily manipulate the trivial LSB based watermarking
method.
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In another work, Sion et al. [19] used collection of tuples (partitions) to embed watermark bits in its
statistics. Statistics were manipulated using distortion tolerance, which is responsible of keeping
values of the database content in a predefined limit. Shehab et al. [32] used optimization approaches
for bringing improvement in the Sion et al method. For the purpose of optimization, hiding function of
technique is targeted, by using Genetic algorithm (GA) to embed watermark in partition statistics of
database.
Additionally, Mailing et al. [33] used GA to hide watermark in database statistics by targeting
frequency domain. Their primary intention was to increase the detection efficiency of watermark;
therefore, the correlation between watermark and database was targeted. However, all of the above-
mentioned watermarking techniques permanently distorted the content of dataset, which cannot be
restored to original version at detection side. This triggered the concept of using reversible
watermarking in the domain of relational database.
Gupta and Pieprzyk [34] for the first time used a reversible difference expansion based
watermarking (DEW) approach for watermarking relational databases. Reversible watermarking
approach can recover embedded watermark as well as restore the database to its original form without
causing any permanent distortion in it. DEW approach of Gupta and Pieprzyk also has implicit
distortion tolerance check capability for both watermark embedding and extraction. A scenario of
additive watermarking attack is also addressed by Gupta et al. [35] using the DEW approach.
Farfoura et al. prediction error expansion (PEE) method utilizes single attribute to embed
watermark [36]. As they used fraction part of the numeric attribute, an adversary can attack fraction
part without disturbing the distortion tolerance of attribute. PEE method have no distortion tolerance
check capability for an attribute, therefore, distortion limit of attribute may be compromised.
Prevailing watermarking techniques [35, 37-39] are less capable of utilizing different attributes for
fitness. Current approaches only check the distortion tolerance of selected attributes, and if the
condition is not fulfilled then the tuple is left unwatermarked. Therefore, there is need for checking
more potential attributes in the same tuple for embedding watermark. Evolutionary methods are
incorporated for achieving optimal solution in the area of pattern recognition [7, 40-43]. Thus, a global
approach is required that utilizes intelligent approaches for refining results of the watermarking
method, by achieving reasonable balance among the elementary properties of a watermarking
approach [24, 44-46].
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2.2 Reversible and Blind Watermarking Technique for Relational
Databases
Relational database is a key multimedia item, and security of its content is a tough job [22, 32, 47-50].
Specially, safeguarding relational database such as those concerned with consumer behavior, weather,
medical, stock market, scientific data, defense, and business is an intimidating job. It is obvious that,
sharing of database with certain organizations is necessary, for better utilization of its content. For
example, intelligent data mining methods help in recognizing interesting outlines in databases, which
helps the process of decision making. Subsequently, sharing of information among its proprietors and
data mining corporations is increasing. For this purpose, watermarking is beneficial for providing
resolution to prohibited copying and relocation of database over the world wide web [50].
Kamran et al. [49] presented a watermarking system that sets the distortion tolerance (DT)
according to the dataset semantics. DT was determined to attain balance among robustness and
distortion. Furthermore, Kamran and Farooq [50] recommended a model for DT for outsourced
classification datasets. Whereby, their informed watermarking does not disturb the classification
capability of the datasets. But, their method is not reversible.
Some motivating database watermarking procedures also observe DT limit for watermarking [34,
35, 37-39]. Though, majority of the prevailing watermarking practices introduce lasting distortion in
the database. However, real life applications like military and medical cannot bear everlasting changes.
Therefore, diverse reversible database watermarking methods are reported in literature [22, 34, 36].
Reversible watermarking methods have the advantage of obtaining the original multimedia object as it
was before watermarking, along with the extraction of watermark information.
DEW method causes high watermarking distortion that is recovered during the detection process.
Furthermore, high DT can result in high false positives (FPs) during detection. On the other side
watermarking capacity may decrease on keeping low DT [35]. Coltuc and Chassery [51] proposed
reversible contrast mapping (RCM) technique to watermark image objects. RCM method has fast
embedding and detection, because no compression is involved in it. In another method, Chen and
Wang [52] devised a new RCM technique having steganalysis for detection and approximation of
hidden message length. They utilized the information obtained by calculating the difference among
cover and watermarked image. Hence, probability information of the pixels belonging to RCM domain
[53] is utilized for watermark detection and approximation.
Payload, imperceptibility, and distortion is enhanced using RCM technique by Maiti and Maity
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[54]. Structural similarity index is utilized to obtain the covariance and variance among watermarked
and cover work. Their focus was to preserve structural information while achieving high watermarking
capacity. RCM method is applied for watermarking in DNA content by Mousa et al. [55]. Without
using any compression method, their approach achieves high watermarking capacity. However, it can
be observed that RCM based watermarking methods do not provide high capacity [51, 54, 55]. This is
mainly, because both watermark bits and extra bits (supporting) are combined as payload bits.
Chen and Wang [52] have first used RCM method to calculate which pixels do not belong to RCM
domain. Afterwards, they have only used those pixels as watermark information that does not belong
to RCM domain. A watermarking technique inspired from RCM method needs to be proposed for
relational database objects. That can embed watermark in every selected attribute, without leaving any
attribute pairs that belong or do not belong to the RCM domain of watermarking. As a result, more
payload can be embedded, while need for encryption or compression and extra storage can be omitted.
2.3 Synonymous Substitution based Watermarking for DNA
Sequences
Research interest in DNA watermarking has increased in previous decade [56]. Initially, information
was secretly hidden in DNA by using the idea of microdots that were used in world war II for
information concealing [57]. In another work, DNA arrangement of Deino-coccusradio-durans was
used for storing song script [58]. Few biological organisms have capability of surviving severe
condition like high temperature, radiation etc. Therefore, their DNA can be useful for hiding data and
the watermark information can also be successfully saved and extracted. Modegi et al. proposed an
information hiding technique that utilizes codon-usage bias-feature to embed data in genomic
sequences [59]. A modified Huffman coding approach was used by Ailenberg et al. [60] to embed
watermark in the DNA sequence. A DNA information hiding method inspired by multiple sequence
alignment was proposed by Yachie et al. [61].
Arithmetic coding was used for degenerative codons by Shimanovsky et al. to hide information in
DNA sequence [62]. Coding region of bacteria DNA was utilized by Arita et al. for storing the
information by applying synonymous substitution technique upon nucleotides [63]. Synonymous
substitution method has advantage of sustaining the amino acid sequence; hence the DNA
functionality is retained. DNA-Crypt model was presented by Heider et al. by combining error
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correction and encryption methods [64]. Difference expansion and lossless compression was used by
Chang et al. to embed watermark in DNA sequence [65]. Likewise, Shiu et al. presented three diverse
DNA data-hiding approaches that comprise insertion, substitution, and complementary approaches
[66]. Mousa et al. presented a reversible contrast mapping method for DNA watermarking [55].
Twenty million replicas of a book, having 5.27 megabyte of data was stored by Church et al. in the
DNA, showing the importance of DNA data storing [67].
Aisling O‟ Driscoll [68] presented a brief overview to certain problems that can grow into probable
difficulties in the large scale acceptance of DNA based storing techniques. The author enlightened how
the problems like decoding, write-once, and sequential read of DNA based storage can be fixed by
using simple schemes. Likewise, write-once problem of rewriteable DNA storage was proposed by
Bonnet et al.[69]. Previous lacking ability of rewriting a data over DNA was resolved by the proposed
method of rewriting the digital data in living cells using directional recombination. 739 kilobyte file
was embedded and extracted using artificial DNA by Goldman et al. [53]. They helped to fulfill the
need for storing huge volumes of data.
Some of the techniques discussed above have certain limitations in DNA watermarking domain.
Some of these techniques cannot be incorporated on living organisms and artificial DNA is used
instead [56-58]. Whereas, those techniques that provide data hiding capability in living organisms, do
not support the handling of losses resulted because of mutation [62, 63]. While, maximum number of
mutations is adjusted by cellular events. For example, through DNA repair, few uncorrected mutations
can result in substantial loss of the watermark.
Similarly, structural arrangement of DNA may be affected by missense and nonsense mutations. As
a result, substantial loss of watermark is observed [58, 63, 67]. Both Missenses and nonsenses
mutations are type of point mutation which exchanges a single nucleotide for another. Missense
mutation converts one amino acid in another amino acid while nonsense changes the nucleotide in
such a way that normal codon turns into a premature stop codon. Frame Shift is type of mutation that
causes insertion or deletion of nucleotides. Silent Mutation is also known as synonymous substitution,
whereby alteration in any nucleotide, results in some different codon. Silent mutation do not disturb
the amino acid sequence and even after mutation the codon translates to same amino acid [70]. The
latest effort utilizes artificial DNA for storing information that involves special situations for survival
and they are not meant to store data on living organisms. Consequently, the information from DNA
cannot be recovered once DNA is affected. Similarly, mutation scenarios are also not given significant
importance. However, the main cause of evolution is mutation, which can result in losing watermarked
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data of DNA.
In order to increase robustness compared to mutations, additional information for error correction
can be used; as a result payload is reduced. Biologically synonymous substitution method, maintains
the amino acid sequence, consequently the DNA functionality is retained. Likewise, it is necessary to
attain balance between different properties of the watermarking system. Therefore, the DNA
watermarking approach needs to have high embedding capacity and more resistant against mutation
attacks.
2.4 Chapter Summary
This chapter reports relevant literature in the area of multimedia watermarking. It was designed to
cover two multimedia objects including relational databases and DNA medium. Next chapter
elaborates the genetic algorithm and difference expansion based reversible watermarking for relational
databases.
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GA AND DEW BASED Chapter 3WATERMARKING FOR DATABASES
Relational databases consist of relations (tables), which contain set of values, organized horizontally
as tuples and vertically as attributes. Addition, alteration, and deletion operation can be performed on
the tuple. Attributes constitutes important properties of a relation, alteration operation can be
performed on them but deletion or addition in attributes can result in losing true meaning of the
relational database. Selection of tuple and then selecting attributes gives a selected cell value that can
be used to embed watermark. These cell values are referred to as target values (TV). The TV‟s are
converted to changed values (CV‟s) after embedding watermark.
Difference Expansion based watermarking technique (DEW) is a reversible technique that can
embed watermark using at least two TVs. In DEW the focus is on how much distortion can be
introduced into the TVs. Limit of distortion for each attribute is different and it is known as distortion
tolerance. Distortion tolerance represents upper and lower limit for each attribute to allow change in its
values. Therefore, no CV should exceed distortion tolerance of its attribute. One can therefore, reduce
distortion by using small value for distortion tolerance; however, this may result in limited amount of
watermark embedding into the cover work. In order to keep the watermarked attribute hidden from the
attacker, we thus select those attributes that are hidden in the neighborhood and cause minimum
distortion in the cover work. The neighborhood represents two contiguous cells located above and two
cells below of TV within the similar attribute.
Another problem with DEW method is the false positive detection. Due to the false positives, exact
extraction of the watermark and restoration of the original data is not possible. However, we have
observed that it is easy to recover the original watermark and original data exactly using a semi blind
detection technique. Therefore, the problem of false positive is resolved using side information.
Similarly, changing order of attributes doesn‟t affect the usability of a relation in a database. However,
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reshuffling attributes at detection side may affect the process of watermark detection [71]. This
problem can also be resolved by passing the order of attributes to the detection module.
Existing DEW approach is mostly unable to increase watermark capacity of the relation without
increasing distortion tolerance of the attributes. Attacker can thus use distortion to predict marked
attributes, which may affect successful detection of the watermark. Consequently, false positives and
changing order of attributes can‟t be tackled at the detection side. In order to solve the above problems
for database watermarking, we thus propose a Genetic Algorithm and Difference Expansion based
watermarking (GADEW) technique. GA and DEW are combined to select suitable TVs for reversible
watermarking, which can help to improve capacity and reduce distortion. Mean and standard deviation
of the watermarked relation are used to evaluate distortion in the attributes. False positives are
eliminated and problem of shuffling attributes at detection side is resolved.
Proposed GADEW technique is able to increase watermarking capacity of the relational database at
a fixed distortion tolerance. Distortion tolerance enforces limits on each attribute so that the value may
not lose its meaning during watermark insertion. Distortion introduced due to watermark insertion is
reduced to minimum by introducing tuple-wise and attribute-wise distortion measures. GADEW is a
reversible watermarking technique that recovers both watermark and cover work exactly as it was
before watermark insertion. Additionally, it is robust against different attacks including, addition,
deletion, sorting, bit flipping, tuple-wise-multifaceted, attribute-wise-multifaceted, and additive
attacks. Random selection of attributes also makes it tough for the attacker to predict watermark.
Problem of the false positive detection is resolved and even addition attack doesn‟t result in false
positive detection.
3.1 Reversible Difference Expansion Watermarking (DEW) Method
Invertible mathematical operations on integer values are undertaken by DEW method [26]. It embeds a
watermark bit in target value (TV) and both original value and watermark bit can be recovered. Two
cell values (TVx and TV
y) are selected using two attributes of a selected tuple of a relation. Three steps
are used for embedded watermark in the selected values. Equation (3.1) shows mathematical
representation of obtaining difference d and average b. Equation (3.2) represents insertion of
watermark bit b and converting d to d'. Equation (3.3) explains how both changed values (CVx and
CVy)
are obtained.
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x yx y(TV +TV )
a = ,d = TV -TV2
(3.1)
d' = 2 d +b (3.2)
x y(d' +1) d'CV = a+ ,CV = a -
2 2
(3.3)
Three steps are used for extracting watermark bit from the changed values and restoring original
values. Equation (3.4) shows how average a and difference d' are obtained. Equation (3.5) is showing
process of extracting watermark bit b from the d'. Equation (3.6) represents the recovery of original
values (TVx and TV
y).
x yx y(CV +CV )
a = ,d' = CV -CV2
(3.4)
2
d'b = d' - 2
(3.5)
x y(d' +1) d'TV = a+ ,TV = a -
2 2
(3.6)
3.2 Genetic Algorithm based Difference Expansion Watermarking
(GADEW) Method
DEW method only embeds watermark into the TV‟s if the CV‟s are within distortion tolerance limit of
their attributes. Distortion tolerance represents upper and lower change that can be tolerated by
attributes. Each attribute have separate limit for distortion tolerance depending upon the properties of
information it is carrying.
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The procedure of Gupta and pieprzyk [34] has a shortcoming as it only checks two TVs in a
selected tuple. Their approach left the tuple unmarked if CVs exceeds the distortion tolerance. It
caused reduction in the embedding capacity of the object. Whereas, exploring capability of different
combinations of TVs in the selected tuple can be handy. Therefore we are exploring capabilities of
different attributes for improving watermarking capacity. Along with improving watermark capacity,
we have also considered reducing distortion of the host object. For this purpose we have employed GA
to embed watermark using DEW method. Instead of just relying on capability of the selected
attributes, it explores combination of attributes in same targeted tuple, and attempts to select the
optimal attribute pair.
Attribute and tuple-wise distortions are combined to decrease the watermarking distortion.
Attribute-wise distortion represents neighborhood cell values of TV. The neighborhood represents
contiguous cells located above and below of TV within the similar attribute. Because each attribute
show values that are nearly independent of the other attributes. Accordingly, TV is matched at definite
position in the neighborhood. Conversely, tuple-wise property only targets the selected tuple and the
distortion within same tuple is taken in consideration. This feature helps to minimize distortion and
develop amenity against the watermarking predictability. The proposed GADEW method makes the
most of watermarking capacity along with decrease tuple and attribute-wise distortion. GA fitness
function uses the above mentioned features for optimization. Tuple and attribute is selected using
message authentication code (MAC) [72].
3.2.1 Message Authentication Code (MAC)
It is a hashing technique that gives a MAC value by using primary key (PK) and secret key (SK). PK
is used to uniquely identify a tuple and attribute in a relation. Where, SK is provided by the owner of
the database. A hashing technique provides integrity along with authenticity of the PK. Equation (3.7)
represents how a MAC value is obtained, here || shows concatenation operator.
MAC value = H(Sk || H(Pk || Sk)) (3.7)
Every time, a different value of PK gives a varied value of H. This property randomizes the
watermark bit and brings difference in indexes for the selection of tuples and attribute pairs. Owner of
the watermark uses SK to provide data integrity as well as authenticity of the PK.
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3.2.2 Chromosome Structure of the Genetic Algorithm (GA)
GA is a global optimization method and idea is taken from nature‟s evolution procedure [48].
Chromosome represents a solution to the given problem. Chromosome structure for the proposed
GADEW technique is provided in Figure 3.1. For each selected tuple only two TVs are used for
embedding. The tuples are selected using MAC [72] procedure. Thus, the size of chromosome is two
times greater than the total selected tuples for watermark.
Total number of chromosomes describes the size of population for GA. Successive populations are
identified as generations of GA. Genetic operators are operated on the existing population and as a
result new population is created. Common operators of GA are crossover, elitism and mutation. Fitness
function determines the fitness of the every chromosome of the population. Normally optimization
process of GA is completed when the total generations are expired or any given fitness condition is
fulfilled. As a result the final solution for the problem is represented by the best chromosome obtained.
Figure 3.1 Chromosome Structure of GA
Crossover operator combines portions of two or additional chromosomes to produce fresh
chromosomes. Elitism operator transfers the best chromosomes of current generation to the next
generation without performing crossover or mutation on them. Whereas mutation operator produces
fresh chromosome by varying values of one or more cells randomly. Mutation is responsible for
discovering the new individuals in the search space.
Collection of different fitness parameters showing separate objectives can be combined into a single
fitness function, such type of fitness functions are known as Multi objective fitness function. Multi
objective fitness function carryout concurrent optimization of several objectives, for example: cost and
performance are two separate objectives. These objectives can be contradictory and cannot be
optimized concurrently; therefore a midway result can be obtained.
TVx1 TV
y1 TV
x2 TV
y2 TV
x3 TV
y3 …………… TV
xn TV
yn
TVx = 1
st target value, x= [2,v]
TVy = 2
nd target value, y= [2,v]
n = Total number of selected tuples
v = Total number of capable attributes, where position 1 is reserved for PK
Two target values
(TVs) of 1st
selected tuple
Two target values
(TVs) of 2nd
selected tuple
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3.2.3 Calculating Fitness
Determining suitability of chromosomes is carried out by GA fitness function. In Multi-objective GA
different objectives/parameters can be combined in single fitness function represented as total cost
(TC). Number of constraints can be fulfilled while using multi-objective GA, which can optimize
multiple objectives concurrently [6, 73]. Three parameters, Capacity related Cost (CrC), Attribute-
wise Distortion (AwD), and Tuple-wise Distortion (TwD) are united in our fitness function. Fitness for
every parameter is obtained and finally summed together as TC. This process is carried out for every
chromosome of the population. Finally, the chromosome with least value of TC is forwarded to the
next module for watermark embedding.
3.2.3.1 Cost related to Capacity
Equation (3.8) is responsible for determining cost related to Capacity (CrC). If distortion tolerance of a
selected TV is not satisfied then the CrC is incremented. In order to insert watermark in the TV of
selected attribute, it is mandatory to check that the CV does not exceed its distortion tolerance.
Equation 3.8 contains CrC, which denotes overall sum of unsuccessful watermarked tuples, whereas,
overall sum of the entire selected tuple of a relation is denoted by n.
Three equations 3.1, 3.2, and 3.3 are utilized to convert the TVs into CVs, by inserting the
watermark bit. CrC is incremented only if the CVs are not fulfilling the distortion tolerance of their
attributes. For example, out of 100 selected tuples only 65 pair of TVs satisfies the limits of distortion
tolerance. Whereas, other 35 pair of TVs does not satisfy the distortion tolerance of their attributes,
therefore the aggregate cost for the current chromosome will be 35.
1
n
k
k
CrC WMUnSuccessful WatermarkBit
(3.8)
0,, ( )
1,
k
k
k
if WatermarkBit is Insertedwhere WMUnSuccessful WatermarkBit
if WatermarkBit is NotInserted
3.2.3.2 Distortion related to Tuple
TC consists of a second factor, which is the resulted distortion of the CV because of watermark
insertion. Transformation of TVs to CVs because of DEW technique signifies distortion. Different
TVs can be selected for embedding watermark bit in a single tuple. However, selection of TVs if done
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randomly can cause distortion [34], therefore those TVs are targeted that causes less distortion.
Process of measuring tuple-wise distortion (TwD) is explained using equation 3.9. Absolute difference
between the resulted CVs and the TVs is obtained that is then summed up as distortion related to tuple.
TwD for a chromosome uses n-CrC that shows all watermarked tuples only.
(3.9)
3.2.3.3 Distortion related to Attribute
Neighborhood of the selected TV is used for obtaining distortion related to attribute referred as
Attribute-wise distortion (AwD). An attribute in a relation may not dependent on any other attribute of
the relation. For this purpose, four immediate values are taken in account that is adjacent to TV. Both
two values above and two values below the TV are considered as neighborhood. Following equation is
used for obtaining AwD.
j=k+2 j=k+2 j=k+2 j=k+2
j=k -2, j=k -2, j=k -2, j=k -2,
j 0 j 0 j 0 j 0
x x y yj j j j
n-CrCx x y y
k k k k
k=1
AwD =
X X X X
- CV - -TV - CV - -TV4 4 4 4
(3.10)
In equation (4.10), n-CrC denotes total number of marked tuples, where X denotes two values
beneath and two values above TV. Every attribute is autonomous from other attributes; hence values
of each attribute have a separate arrangement. Sudden modification in the sequence may become
suspicious for the aggressor as well as raise the distortion. Thus aim is to reduce distortion, so that
aggressor may not calculate the watermarked attribute.
Two cells values below and above of a one selected TV are presented in Figure 3.2. Mean of these
four cell values is obtained. CV is attained after embedding watermark in the TV utilizing DEW
method. Mean value of the neighborhood is deducted from both TV and CV, absolute is obtained for
them. Their difference is summed as the AwD for only one CV. This procedure is done for both TVs
of every selected tuple and their values are finally summed as AwD.
yx x y
k k k k
n-CrC
k=1
TwD = TV CV TV CV
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These fitness parameters are obtained for every chromosome of the GA population. Finally,
chromosome having least TC is selected as the answer of the problem. Minimizing attribute and tuple-
wise distortion of the embedded watermark can provide benefit against the aggressor. DEW base
watermarking method can introduce huge distortion in the underlying object. Aggressor can utilize
these distorted values to guess the watermarked attributes. Probabilities for aggressor are minimized
by adding these fitness parameters in our fitness function called as TC.
Figure 3.2 Calculating Attribute Wise Distortion
3.2.3.4 Overall Cost
Procedure of computing Overall/Total cost (TC) is explained in equation (3.11). TC can be calculated
in equation (3.11) by combining three equations (3.8, 3.9, and 3.10) explaining AwD, TwD, and CrC.
TC = [CrC Wc] +[TwD Wt] [AwD Wa]
(3.11)
W represents a weight vector comprising of Wa, Wt, and Wc. Here, Wc demonstrates weight
allocated to CrC. Whereas, Wa and Wt are the weights allocated to AwD and TwD, respectively. Sum
Absolute difference
of AV and CV
Attribute value of 2nd
neighbor above (X1)
Attribute value of 1st
neighbor above (X2)
Attribute value of 1st
neighbor below (X3)
Attribute value of 2nd
neighbor below (X4)
Attribute value of
first tuple n=1
Attribute value of last
tuple n
Target value (TV) Attribute-wise
distortion (AwD) for one CV only
Difference expansion based
watermarking (DEW)
Changed value (CV)
Average value (AV)
of X1, X2, X3, X4.
Absolute difference
of AV and TV
Calculating difference
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of all these three weights equals to one. These weights can be adjusted accordingly. In order to give
emphasis to certain parameter of the fitness function, the weight value of that parameter can be
increased.
CrC, TwD, and AwD are obtained and summed together as TC, for every chromosome. Superior
chromosomes have smaller value for TC, whereas inferior chromosomes have higher value. This
procedure is performed for all chromosomes of GA population to discover the finest chromosome.
Chromosome with least value of TC is graded as more appropriate.
All fitness constraints can be joined in a solo fitness function or can be used discretely agreeing to
our necessities. GA can be used distinctly for two stages, initial stage can increase capacity and
secondary stage can decrease distortion. Undertaking this one can attain maximum benefit of
achieving more capacity and least distortion. One can also fix weights (W) for each of the three fitness
parameters in order to provide extra worth to certain parameters and fewer to some, by means of this
one can attain the results accordingly.
3.2.4 Example of Obtaining TC, CrC, AwD, and TwD
We are explaining the optimization phenomena for a single selected tuple using an example. Figure
3.3 gives logical explanation of the whole process, by clarifying equations (3.8, 3.9, and 3.10) of the
proposed GADEW method. Initially, watermark is embedded in third tuple of all five attributes (five
TVs) using DEW method. Figure 3.3 (a) represents example dataset and its selected TVs. DEW
method involves two target values TVx & TV
y to embed one watermark bit. In this example the
position of TVx is kept constant and its value is remains the same that is 49. Whereas, selection among
four TVy
is carried out in order to describe the procedure of selecting near optimal TVy. Distortion
tolerance for each attribute is stated at upper most row of the example dataset, which denotes upper
and lower limit for each attribute.
Consequently, changed values (CVs) are obtained by using DEW method on the four TVs one by
one as TVy, whereas TV
x remains constant. Figure 3.3 (b) shows four CVs that were obtained by
transforming one constant TVx, and four CV
ys that were obtained by transforming four different TV
ys.
In order to provide better understanding, only four TVy for four attributes are explained. Only first
three CVys fulfill the distortion tolerance of their attributes, whereas fourth CV
y4 doesn‟t fulfill the
distortion tolerance of its attribute. Therefore, CrC is only incremented for the fourth attribute only,
which is provided in Figure 3.3 (c).
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Target Value
TVx
Target Value
TVy
Equation (3.9) shows process of obtaining TwD, which favors the selection of smallest CV
obtained by DEW method. Figure 3.3 (d) shows CVs of TwD, after applying it on present example.
Consequently, third CVy3 shows smallest distortion that is 2. Equation (3.9) shows process of
obtaining AwD, which favors the choice of smallest CV in perspective of its neighborhood. Figure 3.3
(e) presents CVs after applying AwD on the present example; as a result first CVy1 shows smallest
distortion which is -3.
Figure 3.3 (a) Example dataset followed by its TVs
Attributes Constant
Attribute
First
Attribute
Second
Attribute
Third
Attribute
Fourth
Attribute
Distortion
Tolerance 42-53 59-72 54-62 50-61 58-64
1 48 69 59 54 63
2 46 69 57 53 60
3 49 64 55 53 60
4 42 69 56 54 62
5 48 69 60 55 63
TVs of
selected tuple
TVx1 TV
x2 TV
x3 TV
x4 TV
y1 TV
y2 TV
y3 TV
y4
49 49 49 49 64 55 53 60
Figure 3.3 (b) Resultant CVs by applying DEW method on TV.
CVx
1 CVx2 CV
x3 CV
x4 CV
y1 CV
y2 CV
y3 CV
y4
DEW method 41 46 47 43 71 58 55 65
Figure 3.3 (c) Capacity related cost
CrC equation 3.8 0 0 0 1 0 0 0 1
Figure 3.3 (d) Tuple wise distortion
TwD equation 3.9 7 3 2 5 7 3 2 5
Figure 3.3 (e) Attribute wise distortion
AwD equation 3.10 2 -3 - 2 0 -3 -2 0 1
Figure 3.3 (f) Total Cost
TC equation 3.11 2.75 0 0 1.25 1.5 .25 .5 2
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Figure 3.3 An Example Of Calculating TC, Awd, Twd, And Crc
Total cost (TC) utilizes all of the above values in order to finalize a TVy, which meets the
requirement of all three fitness parameters. As a result only one TVy
is selected, for which TC has
smallest value. Figure 3.3 (f) displays TC for each CVs involved in the example; finally second CVy2
is chosen as near optimal option. In this specific example weights of the equation (3.11) are adjusted
as Wc = 0.5, Wt = 0.25 and Wa = 0.25.
Consequently, watermark can be embedded in second CVy2 using DEW method. Second CV
y2 is
not best (smallest) in every of the fitness parameter; however it is more appropriate to all of the fitness
parameters if matched to other CVs. Use of DEW method alone, which doesn‟t include GA may
simply select the fourth CVy4 and find that it doesn‟t satisfy the distortion tolerance of its attribute. As
a result, no watermark is embedded in the specific tuple and full tuple is left unused. Whereas,
GADEW method can search for potential TVs that support watermark embedding and also favor the
selection of least distorted CV.
Optimization method is elaborated by above example for only one tuple that shows four pair of
TVs. But, chromosome used in the GADEW method only shows one pair of TVs from every selected
tuple of the relation, as shown in figure 3.1. Additionally, CrC for every TV of the chromosome is
added and provided to equation (3.11) for obtaining TC; this procedure is also executed separately for
both TwD and AwD for the same chromosome. Lastly, TC is computed for all the chromosomes that
determine the fitness of all chromosomes and chromosome with smallest TC is finalized as near
optimal choice.
3.2.5 Watermark Embedding
Stages involved in watermark embedding method are represented in Figure 3.4. Embedding process is
divided in three modules. Preprocessing module initializes values for DEW method and to select
suitable fitness parameters for next GA Module. Selection of tuple is carried out by MAC which is a
hashing method. It requires primary key of the selected tuple and a secret key selected by the
proprietor [34]. Every attribute can endure only defined amount of alteration, which denotes upper and
lower limit of the attribute, called as distortion tolerance. Hence, distortion tolerance is diverse for
every other attribute.
Primary key is used to sort the tuples of a relation, for handling tuple deletion attack. Whereas,
attribute wise sorting is carried out alphabetically, by using the names of attributes. Once the sorting is
Multimedia Watermarking Using Intelligent Techniques
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carried out during the embedding phase, then same process can be used during the extraction phase.
We are using three parameters for handling the GA fitness function. In order to set the fitness
according to the requirement, weights (W) of the three parameters are used. Once the initial
parameters of DEW method and weights of the GA fitness function are determined, then the GA
module can start its optimization process.
Figure 3.4 Process Of Watermark Embedding
Near optimal chromosome is generated using GA module by using the suitable GA fitness
function. Number of the selected tuple determines chromosome size, which is twice the size of
selected tuple because two TVs are involved in DEW method. In order to bring variety in the
chromosome, three main components involved in GA are used that are selection, crossover and
mutation. Near optimal chromosome that is best among the available chromosomes of GA module are
forwarded to the insertion module for embedding watermark in it. Watermark bits are obtained using
the MAC technique and CVs are obtained using DEW method. CVs are checked against the distortion
tolerance of its attributes, if the change is within the limit then the CVs are replaced by TVs.
Sorting Tuples and
Attributes
Initial values Relation to be watermarked
Tuples to be watermarked
Private Key
Distortion tolerance
MAC
Preprocessing Module
Selection of GA Fitness parameters
Capacity
Related
Cost
Attribute-
wise
Distortion
Tuple-wise
distortion
GA Module
GA Initialization Size of Chromosome
Size of Population
Termination Criteria
Number of GA Iterations
Fitness evaluation
New Generation
Crossover
Mutation
Elitism
Iterations
Expires
Change values (CV)
using DEW Technique
Comparing distortion
tolerance
Insert Changed values
(CV) in Dataset
Best Individual
Obtaining watermark
bits
Insertion Module
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3.2.6 Watermark Extraction
Figure 3.5 provides the pictorial representation of watermark extraction process. Initial step covers
sorting of attribute and tuple using primary key. Attribute names are used for sorting the attribute and
primary keys are used for sorting of tuples. Secret and Primary key are used by MAC to produce the
watermark bit that can be used in the extraction process. DEW based extraction process restores the
original values along with extracting watermark bits.
If the restored values are not exceeding the distortion tolerance limit and both the extracted
watermark bit and the watermark bit obtained by MAC method are same. Then the restored values are
replaced by the changed values (CVs). Watermark extraction requires the GA chromosome that was
used by embedding phase. Combination of Huffman coding [74] and RSA [75] are used for
compression/decompression and encryption/decryption. RSA is a good public key cryptography
method, whereas Huffman coding is a well-known data compression method.
Figure 3.5 Process Of Watermark Detection
3.3 Results and Analysis
GA toolbox of Matlab 2008b is used for simulation purpose. The value of distortion tolerance is kept
fixed in the experiments. Increasing the value of distortion tolerance may improve chances of
Comparing watermark bits
Restoring original relation using obtained values
Sorting tuple and attributes of a relation
Obtaining Watermark Bits
Obtaining attribute values and bits using DEW detection technique
Checking distortion limit
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watermark insertion in the attributes. As a result, watermark capacity, distortion, and false positive
detection increases.
Two datasets are used for testing the proposed technique. Random-Dataset (R-dataset) contains
randomly generated relations with varying number of tuples from 100 to 10000 and the number of
attributes is fixed to 1000. Each cell of the relation is filled with randomly generated values ranging
from 1 to 1000. Second dataset that we have used in our experiment is the Forest Cover Type dataset
(FCT-dataset), that is provided by University of California, Irvine on its website [76]. This dataset
contains 581,012 tuples and 54 attributes.
A relation of R-dataset containing 10000 tuples and 1000 columns can take 19 seconds to insert
and 22 seconds to detect watermark through DEW technique. Average time of many runs of insertion
and detection algorithm is reported here. Approximately 50% of the TVs are successfully watermarked
that fulfill distortion tolerance of the attributes. Lower bound of distortion tolerance for each attribute
is a fixed value that is 100 and upper bound is 850.
Whereas, GADEW technique completes the whole process of watermark insertion in 13 minutes
and it detects watermark in 8 seconds. Watermark insertion process takes more time because 10
chromosomes are initialized and 10 generations are used to select the optimal chromosome. Increasing
the number of both chromosomes and generations, favors the selection of more optimal chromosome,
as a result time required for GA module is increased. Time consumed by GA module is also dependent
upon the size of chromosome. Size of the chromosome is twice as large as number of selected tuples
because two TVs are selected from each selected tuple.
3.3.1 Capacity Analysis
Probability of successful insertion of watermark increases, when attributes present in a relation are
large in number. Because, GADEW technique can have more attributes to search, in order to select
appropriate attributes for watermarking. As a result, capacity enhances. Less improvement in capacity
is observed, when more tuples are selected to be watermarked as compared to number of attributes.
While, more improvement in capacity is observed, when less tuples are selected to be watermarked.
Watermark is inserted in 44, 105, 475, 1026, 4953 tuples using R-dataset, size of the dataset is set
to 100, 300, 1000, 2000, 10000 accordingly. Success rate increases up to 70 percent, when less
number of tuples is selected to be watermarked. However, success rate decreases down to 37 percent,
when high number of tuples is selected for watermarking.
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Comparison of capacity between DEW and GADEW technique on R-dataset is provided in Figure
3.6. It shows average results of all experiments performed for both GADEW and DEW techniques. As
a result, overall improvement of 12 percent is observed. Using DEW based approach, on average only
36 percent of the tuples are successfully watermarked, while using GADEW approach, 48 percent of
tuples are successfully watermarked.
Figure 3.6 Capacity Comparison Of GADEW And DEW Method Using R-Dataset
Watermark is inserted in 21777, 10945, 9383, 6081, 4136, and 3110 tuples using FCT-dataset.
Average success of watermark insertion using DEW based watermarking is 30.2 %. While after
applying GADEW method, the results improved up to 33.57 %. In this experiment, it is noticed that
for less number of tuples (small chromosome size) the GA achieves high capacity but when the
chromosome size is large, the capacity difference between DEW and the proposed GADEW technique
decreases. Therefore, the decrease in capacity is caused by the selection of large number of tuples to
be watermarked (large chromosome size). To reduce this problem, multiple runs of GA might be
helpful. That is, imposing limit on maximum size of chromosome and running GA separately for each
set of tuples and combining the outcome of all GA runs. Multiple runs may increase the time of
watermark embedding process and the improvement in capacity might remain the same.
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
44 105 475 1026 4953
% o
f S
ucc
esfu
lly W
ater
mar
ked
Tup
les
Number of Tuples Watermarked
DEW
GADEW
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Figure 3.7 shows comparison of capacity between GADEW and DEW method on FCT-dataset.
More improvement in capacity is witnessed using R-dataset compared to FCT-dataset. Because, only
ten attributes are favorable for DEW technique in FCT-dataset. On the other hand, all attributes of R-
dataset are favorable for DEW technique.
Figure 3.7 Capacity Comparison Of GADEW And DEW Method Using FCT-Dataset
3.3.2 Security Analysis
Bit flipping, deletion, and sorting attacks don‟t cause much harm to the watermark according to Gupta,
et al. [35]. Attacker might get the knowledge of distortion in the database and can predict the number
of capable attributes (v) for watermarking [34]. Consequently, the attacker can alter all watermarked
attributes, which can result in losing major portion of watermark. Normally, capable attributes are
small in number; therefore, it is easy for an attacker to predict the value of v. Distortion introduced in
the data or the abrupt changes caused by the insertion of the watermark can provide information about
watermarked attributes to the attacker. This might result in losing most part of the watermark. Two
properties may help in this regard, namely randomness of GA and reduction in distortion. Brief
discussion about each of them is provided as follows:
26
27
28
29
30
31
32
33
34
35
36
3110 4136 6081 9383 10945 21777
% o
f su
cces
sfull
y W
ater
mar
ked
Tup
les
Number of Tuples Watermarked
DEW
GADEW
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3.3.2.1 Randomness of GA
In the proposed technique, value of v doesn‟t determine the selection of attribute. It is the GA that
performs selection between attributes of a selected tuple. GA has introduced randomness in the
selection of attribute; so, it is very difficult for an attacker to predict the marked attributes. This helps
to improve the overall security of the watermark. Thus, GA itself provides help to guard against
attacker.
3.3.2.2 Reduction in Distortion
Random selection of attributes based upon GA doesn‟t provide complete guard against the attacker.
Distortion introduced due to the insertion of watermark may attract the attacker, so minimizing
distortion will also add to the security of the database. At the tuple level, we are trying to select those
two attributes, which are causing minimum distortion in a relation.
At attribute level, our approach tries to hide the watermarked value using neighborhood value.
Invisibility of the watermark also adds to the overall security of the watermark, because it helps to
hide the distortion according to the neighborhood values. Therefore, the attacker will find it hard to
predict the attributes that are marked.
Furthermore, we have also performed experiments related to security of relational database using
two datasets. First, we have performed experiments on R-dataset. Figure 3.8 provides detailed
comparison between standard deviation (Std) of the original-dataset [77] with both DEW and
GADEW method. It clearly shows that the distortion produced in terms of DEW method is high,
compared to GADEW method.
It can be observed from Figure 3.8 that Std of GADEW watermarking technique is closer to the Std
of OrD. On the other hand, DEW based watermarking has high Std compared to the OrD. We can
compare the Std of the whole relation using R-dataset, because in this data, we don‟t have attribute
wise restriction for values, since all the data is randomly generated ranging from zero to one thousand.
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Figure 3.8 Std Comparison Of Ord, DEW, And GADEW Method Using R-Database
Secondly, we have used the FCT-dataset. Only first 10 attributes and 3110 tuples are watermarked
using both DEW and GADEW algorithms. Subsequently, measuring mean and Std of both
watermarked datasets. It is clear that most of the attributes of GADEW watermarked dataset have less
distortion compared to DEW method. Table 3.1 shows mean and Std of four datasets. These datasets
are attained by changing fitness function, during watermark insertion process.
Names of the first 10 attributes are listed in first column of Table 3.1, against which mean and Std
value of each attribute is mentioned. These values are attained after watermark insertion process using
four combinations of fitness functions. Second column shows values for DEW based algorithm. Third
column shows values for GADEW method using fitness value of attribute-wise distortion only. Fourth
column shows values for GADEW method using fitness value of tuple-wise distortion only. Last
column shows GADEW method for both tuple and attribute wise distortion, combined in single fitness
function.
Difference in Mean = | M_DEW - M_OrD|-| M_GADEW - M_OrD| (4.12)
Difference in Std = | S_DEW - S_OrD|-| S_GADEW - S_OrD| (4.13)
0
50
100
150
200
250
44 105 475 1026 4953
Sta
nd
ard
Dev
iati
on o
f fu
ll T
able
Number of Tuples Watermarked
DEW
GADEW
OrD
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Table 3.1: Results Of Reducing Tuple And Attribute-Wise Distortion
M_DEW represents mean, while S_DEW represents Std, value of DEW based watermarked
dataset. M_OrD represents mean, while S_OrD represents Std value of original-dataset. M_GADEW
represents mean, while S_GADEW represents Std, value of GADEW based watermarked dataset.
Difference in mean and Std is calculated using equations (3.12 and 3.13). Difference represents the
improvement. Absolute difference of M_GADEW and M_OrD is subtracted, from absolute difference
of M_DEW and M_OrD. The positive value for Difference in Mean indicates improvement. If
difference is negative it means our results are decreasing. Values of Table 3.1 are used, along with
mean and Std values of OrD to measure the improvement in terms of distortion.
ATRIBUTE
NAME
DEW
Attribute-wise
GADEW
Tuple-wise
GADEW
Tuple &
Attribute wise
Combined
GADEW
Mean Std Mean Std Mean Std Mean Std
ELEVATION 2959.5 280.01 2959.6 279.98 2959.4 279.99 2959.6 279.98
ASPECT 155.63 113.82 155.6 113.7 155.7 113.6 155.6 113.7
SLOPE 14.22 25.83 14.26 25.81 14.26 24.78 14.26 25.81
H_DIST _HY 269.52 213.45 269.5 213.4 269.5 213.2 269.5 213.4
V_DIST _HY 46.57 61.41 46.57 61.19 46.53 60.76 46.57 61.19
H_DIST _RD 2349.7 1559.9 2349.7 1559.9 2349.7 1559.9 2349.7 1559.9
HS_9AM 212.20 33.66 212.2 30.97 212.2 30.82 212.2 30.97
HS_NOON 223.37 29.18 223.4 25.96 223.3 25.81 223.4 25.96
HS_3PM 142.62 43.16 142.6 41.04 142.6 42.39 142.6 41.04
HL_FR_POT 1979.9 1324.7 1980 1324.6 1979.9 1324.6 1980.0 1324.6
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Table 3.2: Results Of Modification In Std And Mean
In Table 3.2, first column shows names of the attributes against which difference for each attribute
is mentioned. Second column shows difference values for GADEW method using attribute-wise
distortion only. Third column shows difference values for GADEW method using tuple-wise
distortion only. Last column shows GADEW method for both tuple and attribute-wise distortion
combined. Negative values are less compared to positive values, which mean we are having overall
reduction in distortion.
3.3.3 Different Attacks
Experimental results of tuple addition, deletion, and bit flipping attacks on GADEW method are
presented and comparisons of GADEW with DEW method are provided on both tuple and attribute
wise-multifaceted attacks. Solution for the problem of secondary watermarking attack is also provided.
Figures presented in this section have detection ratio on vertical axis, which represents the ratio of
successful detection of watermark. Watermark is correctly detected from all the watermarked tuples if
the detection ratio approaches one. If the attacker manages to alter some watermark bits then the ratio
drops below one. Presence of some false positive bits on detection side will increase the detection ratio
from the normal range and in some situations it may become greater than one.
ATRIBUTE
NAME
Attribute-wise GADEW Tuple-wise GADEW Tuple and Attribute wise
Combined GADEW
Difference
in Mean
Difference
in Std
Difference
in Mean
Difference
in Std
Difference in
Mean
Difference
in Std
ELEVATION 0.0160 0.0163 0.0897 0.0193 0.0147 0.0224
ASPECT 0.0170 0.2134 0.0053 0.4329 0.0009 0.1379
SLOPE -0.0345 1.0598 -0.1180 -5.3088 -0.0375 0.0272
H_DIST _HY 0.0365 0.2461 0.0097 -0.0336 0.0006 0.0689
V_DIST _HY 0.0521 0.6555 -0.0302 -0.9078 0.0035 0.2254
H_DIST _RD -0.0503 -0.0062 -0.0634 -0.0208 -0.0555 -0.0056
HS_9AM 0.0484 2.8369 0.0258 2.4839 0.0406 2.6868
HS_NOON 0.0349 3.3747 0.0360 4.7476 0.0110 3.2223
HS_3PM 0.0451 0.7797 0.0352 0.7322 0.0349 2.1220
HL_FR_POT 0.2316 0.0940 0.0707 0.1151 0.1223 0.0808
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This shows that some non-watermarked bits are falsely detected as watermark bits by the detection
algorithm. Horizontal axis represents change in attack percentage (%) according to size of the dataset.
This means altering specific ratio of tuples that are present in the dataset. If a dataset includes 1,000
tuples then altering 10 % means 100 tuples are attacked. Zero (0) % attacks represent detection
without any attack on the dataset, followed by 10 %, 20 %, 30 %, 40 % and 50 % attacks on the
watermarked data.
3.3.3.1 Addition Attack
Adversary performs addition attack by inserting new tuples in order to affect the watermark detection
process. Addition of non-watermarked new tuples can result in increasing amount of false positive
rate. It can also be used by attacker to increase the percentage of non-watermarked tuples in order to
prove that the detection is week. In Figure 3.9 detection ratio is 1.0 when no attack is performed and it
remains 1.0 even after the 50 % new tuples are added into the watermarked dataset, this shows that
GADEW method has solved the problem of false positive detection. Addition attack is not causing
problem because our technique is concerned with watermarked tuples only, non-watermarked tuples or
newly added tuples don‟t affect the process of watermark detection.
Figure 3.9 GADEW method Bit Flipping, Deletion, And Addition Attack Comparison
0
0.2
0.4
0.6
0.8
1
1.2
0 10 20 30 40 50
Det
ecti
on R
atio
Attack Percentage according to Database Size
GADEW Addition Attack
GADEW Bit Flipping Attack
GADEW Deletion Attack
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3.3.3.2 Deletion Attack
Deletion attack is performed for removing the tuples in order to destroy the watermark. Attacker
randomly removes some tuples from the watermarked dataset, hoping that some watermarked tuples
may also get deleted which can result in losing watermark bits. Compared to other tuple wise attacks,
deletion attack can be more harmful because it leaves no chance for correct detection of watermark bit
when a watermarked tuple is deleted. Without performing any attack, detection rate is 1.0 but with
increase in percentage of deletion attack, the ratio of correct detection is also decreasing. It shows that
deletion attack can be more harmful compared to other attacks.
3.3.3.3 Bit flipping Attack
Bit flipping attack is performed for choosing tuples randomly and flipping all the LSBs of attribute in
those tuples. This attack is successful only when sufficient amount of watermarked bits are altered
[35]. In order to increase the difference between results of deletion and bit flipping attack, we have
only flipped LSB‟s of 50 % of the attributes in the attacked tuple, rather than altering whole attributes.
Figure 9 is showing decrease in detection rate as the attack percentage according to dataset size is
increasing. Bit flipping attack seems to be less harmful compared to deletion attack, when only 50 %
of the attributes are altered in the attacked tuple.
3.3.3.4 Sorting Attack
In sorting attack, if the attacker re-sorts the tuples based on any attribute, it doesn‟t affect the detection
algorithm, because we again resort the attribute according to the primary key on the detection side.
Problem with the DEW approach is that if the position of the attribute is changed in the dataset, the
algorithm fails to detect the attributes [71]. Therefore, we sort the attributes according to their names
before insertion of watermark and re-sort them on the detection side accordingly.
3.3.3.5 Tuple-wise-Multifaceted Attack
Tuple-wise-multifaceted attack includes three attacks sequentially carried out on the same
watermarked dataset. Initially addition attack is performed followed by deletion attacks, and finally bit
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flipping attack is applied. After performing tuple-wise-multifaceted attack, comparison between
detection results of both simple DEW and GADEW method is provided in Figure 3.10. Detection ratio
on vertical axis helps us to analyse detection of false positives during detection process.
It is evident through Figure 3.10 that false positive rate is high in DEW, whereas GADEW method
has tackled this problem easily. Detection rate is 1.2 when no attack is performed on DEW method,
showing that we have false positive rate of 0.2. However, for GADEW method detection rate is 1.0,
this shows that GADEW method solves the problem of false positive rate as well. Horizontal axes
represent changes in attack percentage according to size of the dataset. This means altering specific
ratio of tuples that is present in the dataset. 10 % tuple-wise-multifaceted attack indicates 10 %
addition attack followed by 10 % deletion attack and finally 10% bit flipping attack. This attacking
process is also repeated for 20, 30, 40 and 50% tuple-wise-multifaceted attacks.
Figure 3.10 Tuple-Wise-Multifaceted Attacks Comparison Between DEW And GADEW Method
3.3.3.6 Attribute-wise-Multifaceted Attack
Attribute-wise-multifaceted attack comparison for both GADEW and simple DEW method is shown
in Figure 3.11. Attribute-wise-multifaceted attack includes updation attack followed by bit flipping
attack on the selected attribute. Attribute updation attack consists of deleting an attribute and its
contents followed by the addition of another attribute and its contents at the same location. Purpose of
updation attack is not to disturb the position of attribute in the watermarked dataset, because detection
0
0.2
0.4
0.6
0.8
1
1.2
1.4
0 10 20 30 40 50
Det
ecti
on R
atio
Attack Percentage according to Database Size
DEW GADEW
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process can easily be disturbed by not maintaining the position record of attributes in watermarked
dataset [78].
Detection ratio comparison between DEW and GADEW method after applying attribute-wise-
multifaceted attacks is provided in Figure 3.11. These results facilitate us to analyse detection of false
positives during detection process. False positive rate is high in DEW method, whereas GADEW has
tackled this problem easily.
Effects of detection after attribute-wise-multifaceted attacks are higher compared to tuple-wise-
multifaceted attacks because usually in datasets, the attributes are less in numbers compared to the
tuples. So, the probability of destroying watermark is more if attribute wise attacks are considered.
But, on the other hand, integrity of database will also be on stack even if a single attribute is altered, in
some cases database might become meaningless.
Figure 3.11 Attribute-Wise-Multifaceted Attacks Comparison between DEW and GADEW Method
3.3.3.7 Additive Attack
Scenario of additive (secondary watermarking) attack can be tackled using reversible watermarking
[35]. Watermark is inserted in a relation R and we get watermarked relation Rm (RRm). An attacker
changes the watermarked relation Rm to Rm' and re-watermarks Rm' resulting in a new relation
0
0.2
0.4
0.6
0.8
1
1.2
1.4
0 10 20 30 40 50
Det
ecti
on R
atio
Attack Percentage according to Database Size
DEW GADEW
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Rm'm. While Rm'm contains secondary watermark with probability 1 since it has not been modified.
Rm' still contains the initial watermark with a high probability p and attacker removes the initial
watermark with a probability 1-p. (R>Rm>>Rm'>Rm'm).
The judge asks to run detection algorithm for both initial watermark and secondary watermark on
Rm and Rm'm, respectively. Both watermarks are successfully detected in their respective relations
and original relations were restored as R for initial watermark and Rm' for secondary watermark.
Initial watermark is detected in Rm'm with high probability but secondary watermark is detected with
low probability in R. Thus it is clear that the secondary watermark was inserted in the relation already
having initial watermark. Consequently, it is proved that owner of the relation is the one, who inserted
initial watermark into it.
3.4 Chapter Summary
We have used GA to improve the capacity of DEW method in databases, while keeping distortion
tolerance fixed. GA introduces some randomness in DEW technique, thus making it difficult for the
attacker to predict attributes. Security of the watermarking system is also enhanced by reducing the
distortion and minimizing abrupt changes caused by DEW method. This is achieved by two measures
added in the fitness function of GA, first by using the knowledge of the neighborhood values of the
relational database, second by minimizing the distortion introduced by selecting attributes resulting in
minimum distortion. Results are also showing improvement in capacity of watermark.
Consequently, more watermark bits can be embedded in database, while distortion introduced in it
is minimalized. This provides more comfort for the user and leaves fewer options for the attacker to
destroy the watermark. Detection technique of GADEW method resolves problem of reshuffling
attacks on attributes. It is also robust against addition, deletion, sorting, bit flipping, tuple and
attribute-wise-multifaceted, and additive attacks. It has also solved problem of false positive rate at
detection side. In future, we intend to develop a reversible watermarking technique, which can handles
both integer and floating point values present in a single relation.
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REVERSIBLE AND BLIND Chapter 4WATERMARKING FOR DATABASES
Currently, evidence of ownership and patent security is one of the ever increasing concerns of most of
the establishments [6, 24, 44, 79]. However, evidence of ownership of the shared data in the law court
might need some proof. Consequently, before sharing data, a non-disclosure contract may be signed
among the proprietor and the receiver, which forbids the receiver not to claim the proprietorship of the
object or redistribute it. If the receiver violates the contract, then the proprietor is able to prosecute him
in law court, only if when the proprietor can demonstrate his proprietorship on the shared data [71].
Watermarking proves to be useful for managing numerous security concerns faced in
movement of diverse multimedia objects such as DNA , images, database, text etc. [30]. We have
proposed a novel reversible and blind watermarking technique for relational databases called RBW-
RD. The reversibility of the proposed RBW-RD technique is based on the concept of Contrast
Mapping transformation. In context of relational database both of the watermarking techniques (RCM
technique and the proposed RBW-RD technique) are new. The proposed technique is able to achieve
high embedding capacity mainly because of two reasons. Firstly, all three steps of Contrast Mapping
technique are utilized for watermarking. Secondly, there is no overhead of adding side information to
the watermark data. Similarly, watermarking distortion is minimum because only first step out of three
steps causes high distortion, whereby distortion tolerance parameter is exploited to control the
distortion without affecting the embedding capacity. Additionally, in the proposed RBW-RD false
positive rate is minimal because automatic bit checking technique is adopted. The robustness
performance of the proposed RBW-RD is tested against different attacks and comparison with existing
watermarking techniques for relational database shows its effectiveness.
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4.1 Proposed Reversible and Blind Watermarking Technique for
Relational Database
The proposed RBW-RD method maintains the property of reversibility and blindness, besides
achieving high watermarking capacity at less watermarking distortion and FP rate. In order to embed a
single watermark bit using RCM method, two values are required [51, 52, 54, 55]. Whereas, the
selection of tuples, attributes, and watermark bits are carried out using MAC Hashing technique (H)
[47]. Therefore, the proposed RBW-RD technique is able to resist different types of attacks, such as
tuple deletion, addition, sorting, and bit flipping attack. In the proposed RBW-RD technique, selection
of tuples, attribute pairs, and watermark bits are performed using message authentication code (MAC),
which is explained at section 3.2.1 using equation (3.7).
4.1.1 Automatic Bit Checking
Certain watermarking attacks can result in huge number of FPs during watermark detection. FPs
falsely detects the faulty watermark and restores the false pair, which affects the extraction of
watermark and also disturbs the exact recovery of the original relation (R). However, FPs can be
reduced by introducing automatic bit checking technique at detection side.
In the proposed RBW-RD technique, for each selected tuple, an automatic watermark bit is
generated using H [34]. The obtained watermark bit is embedded in a selected attribute pair using
proposed RBW-RD technique. On extraction side, for each selected attribute pair, the automatically
generated watermark bit (b) is obtained. It is compared with the extracted watermark bit (b‟), if not
matched then the original attribute pair is not restored. Thus, results in less FPs and reducing distortion
of the restored relation (RR), caused by different attacks. Thus, number of FPs are reduced, as a result
the distortion of the RR is also minimized.
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4.1.2 RCM Transform
In order to embed watermark bit, two attributes x and y are selected from the same selected tuple using
H. Further, integer and fraction portion are separated for both of the attributes, which are denoted as
Int_x, Frac_x and Int_y, Frac_y. To prevent underflow and overflow, lower and upper limits [0, L] are
defined as RCM domain. It is shown in Figure 4.1. Upper limit (L) ensures that values may not exceed
word-length of each attribute that is determined by left shift technique (L=2t-1), where, t (word-
length) represents number of bits used to represent an integer value for the attribute. The forward
RCM transform converts original integer pair (Int_x, Int_y) into watermarked integer pair (Int_x′,
Int_y′) using equation (4.1).
Int_x' = 2Int_x - Int_y, Int_y' = 2Int_y - Int_x (4.1)
To prevent overflow/underflow, the conversion is restricted to RCM domain that is represented by
[0, L] x [0, L] and is given by the following equation.
0 2Int_x - Int_y L, 0 2Int_y - Int_x L
(4.2)
Figure 4.1 RCM Domain For 8-Bit Attribute
0
50
100
150
200
250 0 50 100 150 200 250
Y
X
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The inverse RCM transform restores the watermarked integer pair (Int_x′, Int_y′) back to original
integer pair (Int_x, Int_y) using equation (4.3). Ceiling(x) = ⌈x⌉ represents a ceiling function, which
gives smallest integer not less than⌈x⌉.
2 1 1 2
3 3 3 3Int_x Int_x' + Int_y' ,Int_y Int_x' + Int_y'
(4.3)
According to equation (4.2), the integer pair (Int_x, Int_y) belongs to RCM domain if the integer
values of its watermarked pair (Int_x′, Int_y′) fulfills the two constrains 0 ≤ Int_x'≤ L and 0 ≤ Int_y'≤
L; otherwise it does not belongs to RCM domain.
4.1.3 Distortion Tolerance (DT) Check
Some attributes of a relation do not tolerate much distortion. Therefore, distortion tolerance (DT)
keeps a check on the values of the attributes so that the change may not exceed a certain limit [22, 34].
DT check is incorporated in the proposed RBW-RD approach to avoid too much distortion and thus
keep the usability of the RW intact.
The forward RCM transforms the pair (Int_x, Int_y) using equation (4.1). Thus, causing distortion
for Int_x as Int_x' - Int_x = 2Int_x - Int_y - Int_x = Int_x - Int_y, and distortion for Int_y as Int_y' -
Int_y = Int_y - Int_x. Let δjk be the DT for different attributes j. DT provides upper (δInt_xj1) and
lower (δInt_xj2) limits for an attribute, depending upon the usability of values present in that attribute.
Value of DT check may be different for each attribute, therefore, the pair (Int_x, Int_y) is transformed
only if the watermarked value are with in DT Limit, i.e δInt_xj2 < Int_x - Int_y < δInt_xj1 && δInt_yj2
< Int_y - Int_x < δInt_yj1.
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4.1.4 Watermark Embedding
Watermark embedding process is explained in Figure 4.2. Selection of tuple, attribute pair, and
watermark bit is performed using H [34, 35]. Fraction and integer portion is separated and watermark
bit is embedded using one of the following three steps of RBW-RD Technique.
1. If both integers belong to RCM domain, satisfy DT check, and are not odd, then the pair is
transformed using equation (4.1) and LSB of Int_x‟ is set to „1‟ and watermark bit is embedded at
LSB of Int_y′.
2. If both integers belong to RCM domain, satisfy DT check, and are odd, then LSB of Int_x is set to
„0‟ and watermark bit is embedded in LSB of Int_y.
3. If any of the integer values don‟t belong to RCM domain or don‟t satisfy DT check, then LSB of
Int_x and Int_y are saved in the LSBs of fraction portion (Frac_x and Frac_y). While LSB of Int_x
is replaced with „0‟ and LSB of Int_y is replaced with watermark bit.
Finally, the watermarked attribute pairs are replaced with the original attribute pairs after combining
integer and fraction portion of both attributes.
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Figure 4.2 Block diagram Of Watermark Embedding Phase.
No
Yes
Yes
Yes
No
No
Select two attributes from the tuple using H
Select tuple and generate watermark bit b using H
Separate fraction and integer portion of both attributes
Select a Relation R, Secret Key SK, and Hashing Function H
For each tuple in the relation
No
Yes
Frac _x'=LSB(Int_x)
LSB(Int_x’)=0
(Int_x,Int_y) belong to RCM
Domain ?
(Int_y,Int_x) are both odd ?
Transform Int_x & Int_y
using (2)
Frac _y'=LSB(Int_y)
LSB(Int_y')=b
LSB(Int_x')=0
LSB(Int_y')=b
LSB(Int_x')=1
LSB(Int_y')=b
Combine integer and fraction portion and overwrite (x, y), with (x', y')
End of sequence?
Start
Stop
(Int_x,Int_y) satisfy DT ?
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Figure 4.3 Block Diagram Of Watermark Extraction Phase.
Yes
No
Select two attributes from the tuple using H
Select tuple and generate watermark bit b using H
Separate fraction and integer portion of both attributes
Select a Relation R, Secret Key SK, and Hashing Function H
For each tuple in the relation
If LSB(Int_x')==1 ?
Combine integer and fraction portion and overwrite (x', y') with (x, y) if b == b'
End of sequence ?
Start
Stop
LSB(Int_xx)=1 LSB(Int_yy)=1
b'=LSB(Int_y')
Int_x =Int_xx
Int_y= Int_yy
b'=LSB(Int_y')
LSB(Int_x)=LSB(Frac_x')
LSB(Int_y)=LSB(Frac_y')
b'=LSB(Int_y')
LSB(Int_x')='0'
LSB(Int_y')= '0'
Int_x,Int_y=Inverse_t
ransform(Int_x',
Int_y') using (4)
Yes
Yes
No
No
No
Yes
(Int_xx,Int_yy) satisfy DT ?
(Int_xx,Int_yy) belong to RCM
Domain ?
Int_xx=Int_x' Int_yy= Int_y'
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4.1.5 Watermark Extraction
Watermark extraction process is provided in Figure 4.3. Selection of tuple, attribute pair, and
watermark bit is performed using H [15, 17]. Before applying RBW-RD technique for watermark
extraction, the integer and fraction portion is separated for each attribute pair. Accordingly, watermark
is extracted using one of the following three steps of RBW-RD Technique.
1. If LSB of Int_x′ is „1‟, the integer pair belongs to RCM domain and satisfies DT check. Therefore,
the LSB of Int_y′ is obtained as detected watermark bit. LSB of Int_x′ and Int_y′ are set to "0"
and the original integer pair (Int_x, Int_y) is restored by inverse RCM transform using equation
(4.3).
2. If LSB of Int_x′ is „0‟, then LSB of Int_y′ is saved before setting LSBs of integer pair
(Int_xx′, Int_yy′) to „1‟. If both integers (Int_x′, Int_y′) belong to RCM domain and satisfy DT
check, then LSB of Int_y′ is obtained as detected watermark and LSBs of Int_x′ and Int_y′ are set
to „1‟.
3. If LSB of Int_x′ is „0‟, and the integer pair (Int_x′, Int_y′) does not belong to RCM domain or
satisfy DT check, then LSB of Int_y′ is extracted as obtained watermark bit. The original integer
pair (Int_x, Int_y) is restored by replacing LSBs of (Frac_x′, Frac_y′) with LSBs (Int_x′, Int_y′).
LSB of Int_y′ is extracted in all the three cases and matched with the automatically generated
watermark bit. If both are same, then watermarked attributes of the selected tuple are restored. Lastly,
integer and fraction portion of both attributes are combined. Embedding algorithm is devised to
exactly restore the relation and the watermark bit, while utilizing every selected attribute for achieving
high watermarking capacity. Figure 4.4 provides detailed algorithm of both watermark embedding and
extraction process.
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Figure 4.4 Embedding and Extraction Algorithms of The Proposed RBW-RD Technique
Watermark Embedding Algorithm ( R, Key SK, λ, α) Input, Original Relation R, Secret Key SK, Fraction of tuples 1/ λ, mark-able attributes α Output: Watermarked Relation RW 1 for each tuple ti in R 2 loop 3 if H(ti.PK || SK) mod λ = 0 // mark this tuple 4 x = ti.(H(ti.PK || SK ) mod α); // mark attribute x 5 y = ti.(H(ti.PK/2 || SK) mod α); //mark attribute y 6 b = LSB(H(ti.PK || SK)); 7 Frac_x=Bin(Get_frac(x)); 8 Int_x=Bin(Get _int(x)); 9 Frac_y=Bin(Get_frac(y)); 10 Int_y=Bin(Get _int(y)); 11 domain=check_domain(Int_x, Int_y) 12 if domain==1, DT=DT_check(Int_x, Int_y) 13 if DT ==1, odd=check_odd(Int_x, Int_y); 14 if odd==0 15 Int_x’,Int_y’=transform(Int_x,Int_y); 16 Int_x’(length(Int_x’))='1'; 17 Int_y’(length(Int_ y’))=b; 18 end 19 if odd==1 20 Int_x’ (length(Int_x))='0'; 21 Int_y’ (length(Int_y))= b; 22 end 23 end, end 24 if domain==0 || DT==0 25 Frac _x’(length(Frac _x))=LSB(Int_ x); 26 Int_x’ (length(Int_x))='0'; 27 Frac _y’(length(Frac _y))=LSB(Int_ y); 28 Int_y’(length(Int_ y’))=b; 29 end 30 x’=Dec((Int_x’)||.||(Frac_x’)); 31 y’=Dec((Int_y’)||.||(Frac_y’)); 32 end 33 end loop
Watermark Detection Algorithm ( RW, Key SK, λ,α) Input: Watermarked Relation RW, Secret Key SK, fraction of tuples 1/ λ, mark-able attributes α Output: Restored Relation RR 1 for each tuple ti in RW 2 loop 3 if H(ti.PK || SK) mod λ = 0 // mark this tuple 4 x’ = ti.(H(ti.PK || SK) mod α); // mark attribute x 5 y’ = ti.(H(ti.PK/2 || SK) mod α);//mark attribute y 6 b = lsb(H(ti.PK || SK)); 7 Frac_x’=Bin(Get_frac(x’)); 8 Int_x’=Bin(Get _int(x’)); 9 Frac_y’=Bin(Get_frac(y’)); 10 Int_y’=Bin(Get _int(y’)); 11 if LSB(Int_x’)== '1' 12 b’=LSB(Int_y’); 13 If b’==b 14 Int_x’ (length(Int_x’))='0'; 15 Int_y’ (length(Int_y’))= ‘0’; 16 Int_x, Int_y=transform(Int_x’, Int_y’); 17 end 18 else if LSB(Int_x’)== '0' 19 Int_xx (length(Int_x’))='1'; 20 Int_yy (length(Int_y’))= ‘1’; 21 domain= check_domain (Int_xx, Int_yy); 22 If domain==1, DT=DT_check(Int_xx,Int_yy); 22 if DT ==1, b’=LSB(Int_y’); 23 If b’==b 24 Int_x =Int_xx; 25 Int_y= Int_yy; 26 end 27 end, end 28 If domain==0 || DT==0 29 b’=LSB(Int_y’); 31 If b’==b 32 Int_x (length(Int_x’))=LSB(Frac_x’); 33 Int_y (length(Int_y’))=LSB(Frac_y’); 34 end 35 end 36 end technique 37 x= Dec((Int_x)||.||(Frac_x’)); 38 y= Dec((Int_y)||.||(Frac_y’)); 39 end 40 end loop
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4.1.6 Analyzing Three Steps of RBW-RD
Analysis of the three steps of the proposed RBW-RD technique is given below.
4.1.6.1 First Step
During first step, Int_x′, Int_y′ are obtained after applying forward RCM transform using equation
(4.1). The LSBs of Int_x', Int_y' are lost because watermark bit is embedded. At detection side, LSBs
of Int_x' and Int_y' are set to „0‟ after extracting watermark bit. This step along with the ceiling
function of inverse RCM transform equation (4.3) ensures the exact restoration of original values
(Int_x and Int_y). The transformation (2/3 and 1/3) using equation (4.1) and restoration (1/3 and 2/3)
using equation (4.3) of RBW-RD technique are the same, except setting LSB‟s of Int_x' and Int_y' to
„0‟ and using ceiling function at the detection side [51].
4.1.6.2 Second Step
The inverse RCM transform in equation (4.3) can exactly restore the original values (Int_x, Int_y),
except when Int_x' and Int_y' are both odd [51]. LSB of „1‟ means an odd integer number. From
equation (4.1), it follows that (Int_x', Int_y') are both odd integers only if (Int_x, Int_y) are odd
integers too.
4.1.6.3 Third Step
Third step embeds watermark in those attribute pairs that do not belong to RCM domain or do not
fulfill DT check. Whereas, previously proposed RCM techniques do not use the third step for
watermark embedding [51, 52, 54, 55]. Therefore, in the proposed RBW-RD technique, third step
helps to increase the watermarking capacity of the relational database. Third step embeds watermark
bit at LSB of int_y, whereas LSB of int_x represent attribute pair of the watermark.
In order to preserve the LSBs of watermarked pair (Int_x′, Int_y′), LSBs of fraction portion (frac_x,
frac_y) are used. LSBs of the fraction portion can tolerate small change. However, there is less chance
of losing LSB of fraction portion because only third step of RBW-RD technique utilizes this
procedure. Moreover, if the fraction portion is attacked even then the watermark can be recovered
exactly. In the proposed RBW-RD approach, only integer portions are targeted for watermark
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embedding because integer has less possibility to be attacked, whereas fraction portion can easily be
manipulated.
4.1.7 Reduction in Watermarking Distortion
Table 4.1 provides probability of watermark embedding for three steps. In order to calculate
probabilities for all three steps of the proposed RBW-RD technique, the LSB representation for the
attribute pairs is analyzed. If 50 % of the total selected attribute pairs belong to RCM Domain then,
probability of using first step of the proposed RBW-RD technique is 0.375 and probability for second
step is 0.125. Whereas, probability for third step is .50, because 50% of the total selected attribute
pairs don‟t belong to RCM domain.
The probability of using first step is expected to be 0.375 only. However combined probability of
second step and third is high (0.625). First step of RBW-RD is responsible for causing high distortion
because it uses equation (4.1) for watermarking. Accordingly, distortion caused by RBW-RD
technique will be less, because probability of using first step is less. However, second and third step of
the proposed RBW-RD technique have negligible effect on watermarking distortion. Thus, distortion
caused by RBW-RD technique is less while the watermarking capacity is high because all the three
steps are utilized for watermarking.
Incorporating DT check can further reduce the use of first step, thus beneficial to reduce overall
Table 4.1: Probability Of Watermarking For All Three Steps
First Step Second Step Third Step
Before Watermarking (int_x, int_y) (int_x, int_y) (int_x, int_y)
LSB representation of attribute
pair before watermarking (0,0) (0,1) (1,0) (1,1)
(0,0) (0,1)
(1,0) (1,1)
If 50 % of the total selected
pairs of attribute belong to
RCM Domain
(0.50) (0.50)
Probability of using each step (0.375) (0.125) (0.50)
After Watermarking (int_x',int_y') (int_x′,int_y′) (int_x′,int_y′)
LSB representation of attribute
pair after watermarking
(1,0)
(1,1)
(0,0)
(0,1)
(0,0)
(0,1)
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watermarking distortion of the relation. If DT is not fulfilled at first step, then the third step will be
used for watermarking, which does not causing much distortion. Thus, watermarking capacity of the
relation is not affected while satisfying the DT check. Altering the third step is very useful both for
capacity and distortion because despite very low value of DT check, the watermarking capacity of
database is not affected
4.2 Improvements of The Proposed RBW-RD Technique Over RCM
Technique
This section highlights different advantages of the proposed RBW-RD technique by comparing it with
RCM technique for relational database watermarking. The proposed RBW-RD technique achieves
high watermarking capacity compared to RCM techniques [51, 52, 54, 55], because watermark bits are
successfully embedded even if the transformed pairs do not belong to RCM domain. Additionally,
there is no need of extra storage, which is another reason for increase in watermark capacity.
Therefore, utilizing both integer and fraction portions for watermark embedding helps to increase
watermarking capacity.
On the other hand, DT factor is added for providing extra control to the proposed RBW-RD
technique, which is absent in RCM technique. It helps to observe limitations for each attribute so that
transformed value may not exceed their DT level. As a result overall distortion is minimized without
affecting watermarking capacity.
At detection side automatically generated watermark bits are matched with extracted watermark
bits. As a result, FPs caused by addition or bit flipping attacks are minimized. Thus, mean and std
measure of RR gets closer to mean and std of R, thereby attaining better reversibility
4.2.1 Increased Watermarking Capacity
Third step of RCM technique is not utilized for watermarking. Further, LSBs of the integer pair is
separately saved, along with the watermark information [51, 52, 55]. However, third step of the
proposed RBW-RD technique do not save LSB of int_x separately. Thus, it helps blind detection,
without requiring any additional compression/encryption. Furthermore, watermark bit can be
embedded at LSBs of int_y, thereby increasing watermarking capacity of the algorithm.
In the proposed RCM technique, LSBs of fraction portion (frac_x, frac_y), is utilized for preserving
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LSBs of watermarked pair (Int_x′, Int_y′). However, LSBs of the fraction portion can bear minor
alteration. Moreover, watermark can be accurately restored even if the fraction portion is attacked.
Thus, the proposed RBW-RD technique achieves more capacity and better reversibility compared to
RCM technique.
Figure 4.5 illustrates improvement in capacity by presenting true positives (TPs) for all three steps
of proposed RBW-RD technique. Fraction (1/λ) of tuple attacked is shown at horizontal axes, while
embedded watermark is shown on vertical axes. Experiments are conducted on a relation of 10,000
tuples consisting of 11 attributes, out of which first attribute is primary key and remaining 10 attributes
are used for watermark embedding. Each attribute contains numeric value, generated randomly from 0
to 999. Whereas, 0.17 fractions of tuples are used for watermarking and DT limit for all attributes is 0
to 500.
4.2.2 Less Distortion with Same Capacity
Three different values of DT check are used to measure overall distortion of RBW-RD watermarking.
Distortion measure for RW using the three values of DT check are represented as A = DT (0-250), B =
DT (0-500), and C= DT (0-999). It is evident that the distortion can be minimized while capacity
Figure 4.5 Capacity Comparisons After Deletion Attack
0
200
400
600
800
1000
1200
1400
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.79 0.89
Emb
edd
ed W
ater
mar
k
Fraction of Tuple Attacked (1/ λ)
TP_First Step
TP_Second Step
TP_Third Step
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remains the same. Depending upon the distortion requirement of the relation, if the distortion
requirement of the relation is very less even then the watermark is embedded without affecting its
capacity.
Column 1, 2, and 3, of Table 4.2, show distortion measure (mean, std) for three values of DT check
for RW. Each row represents mean and std of a particular attribute of a relation. Therefore, for 10
attributes, mean and std is provided in 10 tuples of the table. Whereas, last tuple provide mean and std
for the whole relation.
Equation (4.5) and (4.6) are used to measure improvement in distortion (mean and std) [22], as
provided in Table 4.3. A, B, and C represents (distortion) mean and std measures of RW using proposed
RBW-RD technique with values of DT check ranges from 0-999, 0-500, and 0-250. In (5) and (6),
absolute difference of A and O (mean, std measure of R) is subtracted, from absolute difference of C
and O. The positive value of difference for an attribute implies improvement. If difference is negative,
it means our results are decreasing. Maximum improvement can be observed in terms of distortion,
when improvement between A and C (relations) is calculated using equations (4.4) and (4.5).
Table 4.2: Mean And Std (Distortion) By Varying DT For Three Watermarked Relations
DT 0-250 DT 0-500 DT 0-999
DA_Mean DA_Std DB_Mean DB_Std DC_Mean DC_Std
518.8760 290.2835 518.9211 290.2631 518.7872 290.4524
470.9878 287.6695 470.9557 287.7225 471.0778 288.5631
489.9092 281.8385 489.8773 281.8745 489.8196 282.2471
486.1661 281.9845 486.1598 282.0138 485.9054 282.2217
500.1412 256.4161 500.1466 256.4236 500.2926 256.8708
508.9844 269.4938 508.9803 269.5078 509.1231 269.9312
510.5157 297.8284 510.5157 297.8284 510.5255 297.9881
447.5397 301.5168 447.5672 301.5046 447.5090 301.7310
433.2113 269.5894 433.2129 269.5898 433.3299 269.9873
511.8894 274.2112 511.8894 274.2112 511.8810 274.2121
487.8221 13.9421 487.8226 13.9391 487.8251 13.8975
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0
500
1000
1500
2000
0-250 0-500 0-999
Emb
edd
ed W
ater
mar
k
Distortion Tolerance (DT) Limit
TP_Third Step
TP_Second Step
TP_First Step
A C C O A OImprovement (in Mean) of D over D = | D _Mean - D _Mean| - | D _Mean- D _Mean| (4.4)
A C C O A OImprovement (in Std) of D over D = | D _Std - D _Std | - | D _Std - D _Std | (4.5)
Figure 4.6 Measuring Capacity Against Varying DT
Table 4.3: Measuring Improvement In Distortion By Using Different DT
Improvement of (relation) DA
over DC
Improvement of (relation) DB
over DC
Improvement of (relation) DA
over DB
Mean Std Mean Std Mean Std
0.0888 0.1690 0.0627 0.1519 0.0261 0.0171
0.0558 0.8649 0.0237 0.8406 0.0321 0.0244
0.0896 0.4060 0.0577 0.3726 0.0319 0.0334
0.2607 0.2372 0.2544 0.2079 0.0063 0.0293
0.1348 0.4547 0.1402 0.4473 -0.0054 0.0074
0.1187 0.4373 0.1146 0.4234 0.0041 0.0140
-0.0056 0.1597 -0.0056 0.1597 0.0000 0.0000
0.0307 0.2118 0.0172 0.1996 0.0135 0.0122
0.0896 0.3979 0.0912 0.3975 -0.0016 0.0004
0.0084 0.0004 0.0084 0.0004 0.0000 0.0000
0.0871 0.2379 0.0764 0.2332 0.0107 0.0122
Improvement of DA over DC=abs(DC_Mean-DO_Mean)-abs(DA_Mean- DO_Mean)
Improvement of DA over DC =abs(DC_Std- DO_Std)-abs(DA_Std- DO_Std)
Improvement of DB over DC=abs(DC_Mean- DO_Mean)-abs(DB_Mean- DO_Mean)
Improvement of DB over DC=abs(DC_Std- DO_Std)-abs(DB_Std-O_Std)
Improvement of DA over DB =abs(DB_Mean- DO_Mean)-abs(DA_Mean- DO_Mean)
Improvement of DA over DB =abs(DB_Std- DO_Std)-abs(DA_Std- DO_Std)
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Figure 4.6 shows that the watermark capacity remains the same for different values of DT checks.
Values of DT check are shown at horizontal axes, while embedded watermark is shown on vertical
axes. It is evident that first step causes high distortion as compared to third step. Therefore, reducing
value of DT check minimizes the use of first step, as a result overall distortion in RW is reduced.
Additionally, no effect on capacity is noticed by reducing values of DT check. Because, if first step do
not meet the requirement of the attribute (DT check) then third step is used.
4.2.3 Reducing FPs and Distortion Because of Addition and Bit Flipping Attack
Bit flipping attack inverts LSBs of all the attributes of the selected tuple [47, 79]. High rate of bit
flipping attacks can cause high FPs. Consequently, the R is not exactly restored. Table 4.4 shows the
improvement because of incorporating (with) bit checking (F) against no (without) bit checking (E)
technique. Distortion measure (mean, std) is obtained on RR.
Initially, the improvement in distortion of RR is measured after performing 10 % bit flipping attacks,
the results show improvement with bit checking incorporated. While measuring distortion after 80%
bit flipping attacks, show high improvement compared to previous improvement. Addition attack adds
new tuples to the selected relation in order to weaken the watermark detection process [22, 34, 47]. As
shown in Figure 4.7, addition attack is causing FPs, whereas bit checking technique detects FPs.
Increase in the ratio of addition attack also increases the number of FPs. Bit checking technique helps
to tackle FPs, as a result the difference between FPs and TPs increases.
Figure 4.7 Watermark Detection After Bit Flipping Attack (With BitCheck)
0
200
400
600
800
1000
1200
1400
1600
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.79
Det
ecte
d W
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mar
k
Fraction of Tuple Attacked (1/ λ)
TP_Third Step
TP_Second Step
TP_First Step
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FPs are detected and eliminated as shown in Figure 4.8, while FP rate is high in Figure 4.9. Figure
4.9 shows increase in the use of 1st step of RBW-RD technique, which causes high FPs and distortion.
Increasing the ratio of bit flipping attack also increases FPs detection and distortion in RR.
Table 4.4: Effect Of Bit Checking On Distortion, Because Of FP‟s Caused By Bit Flipping And
Addition Attack (DT Check = 0-250)
Improvement of (relation) DF over DE
10 % Attack 90% Attack
Bit Flipping Addition Bit Flipping Addition
Mean Std Mean Std Mean Std Mean Std
0.0724 0.2119 -0.0270 0.1206 0.2648 1.5193 0.0456 0.2255
0.0196 0.6699 0.0262 0.0506 0.2230 4.9101 0.1310 -0.154
0.1580 0.4248 0.0495 0.0535 0.2327 2.5616 0.0013 0.4253
0.0050 0.1075 -0.0122 0.0773 0.0265 1.6965 0.0110 -0.134
0.1065 0.3306 0.0150 0.0314 0.3459 1.6317 -0.0219 0.1600
-0.0110 0.1769 -0.0220 0.0311 -0.0120 1.7467 -0.1750 0.2491
-0.0010 0.2205 0.0010 0.0525 0.0298 1.7731 0.0415 0.1912
0.1440 0.1831 0.0428 0.0368 0.5768 1.5695 -0.0223 0.2214
0.1571 0.1594 0.0335 0.0056 0.6417 1.4351 0.0903 0.1677
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0095 -0.0040
0.0649 0.2484 0.0106 0.0459 0.2328 1.8843 0.0110 0.1347
Improvement of DF over DE = abs(DF_Mean-O_Mean)-abs(DE_Mean-O_Mean)
Improvement of DF over DE= abs(DF_Std-O_Std)-abs(DE_Std-O_Std)
Figure 4.8 Watermark Detection After Addition Attack (With And Without Bitcheck)
0
500
1000
1500
2000
2500
3000
3500
0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.79 0.89
Det
ecte
d W
ater
mar
k
Fraction of Tuple Attacked (1/ λ)
Without_BitCheck
With_BitCheck
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Figure 4.10 shows that bit checking minimizes the FPs for both bit flipping and addition attacks.
Horizontal axis represents the fraction of tuple attacked, whereas vertical axis represents FP detection
of watermark. As bit flipping inverts LSB of Int_x and Int_y, therefore it causes high FPs. Thus, bit
checking can eliminate more of them. FPs caused by addition attack are also tackled using bit
checking, however, comparatively less FPs are observed compared to bit flipping attack. Further, it
can be observed that increasing attack level also increases number of FPs.
4.3 Robustness Analysis of The Proposed RBW-RD Method
In this section, results and discussion of addition, bit flipping, and subtraction attack are provided for
the proposed RBW-RD technique. The proposed technique utilizes both integer and fraction portion
for watermarking. However, attacks are carried out on integer portion only, because attacking fraction
portion does not harm the watermark detection process. It is illustrated that both bit flipping and
subtraction attacks have almost same effect on watermark detection process. No FP is detected using
proposed RBW-RD technique, while FPs are detected only after addition or bit flipping attack.
Whereas, sorting attack does not affect watermark detection process because H is used to select the
tuples to be marked [31]. In addition, the detection process is completely blind.
Figure 4.9 Comparisons Of FPs Between Bit Flipping And Addition Attack
0
100
200
300
400
500
600
700
800
900
1000
0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.79 0.89
FP D
etec
tio
n o
f W
ater
mar
k
Fraction of Tuple Attacked (1/ λ)
Addition Attack
Bit Flipping Attack
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In bit flipping attack, LSB‟s of integer portion for all the attributes of the selected tuple are changed.
Bit flipping attack is executed for different percent of tuples, ranging from 10 - 80 %. Different
fractions of tuple are selected for watermark embedding and detection, ranging from 0.01 to 0.64.
Figure 4.10 FP&TP Detection After Bit Flipping Attack(Without BitCheck)
Figure 4.11 RBW-RD Capacity Comparison Of Simple Relation And After 10%
Subtraction, Addition, & Bit Flipping Attacked
0
1000
2000
3000
4000
5000
6000
7000
0.01 0.08 0.16 0.25 0.32 0.4 0.48 0.56 0.64
Det
ecte
d W
ater
mar
k
Fraction of tuple selected to be watermarked (1/ λ)
No Attack
10 % Subtraction
10 % Addition
10 % Bit Flipping(50%)
0
200
400
600
800
1000
1200
1400
1600
1800
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.79
Det
ecte
d W
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mar
k
Fraction of Tuple Attacked (1/ λ)
FP_TP_Third Step
FP_TP_Second Step
FP_TP_First Step
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Results of watermark detection after bit flipping attack are shown in the Figure 4.11. It provides
successful detection of watermark after 10% to 80% bit flipping attacks on .01 to .64 fractions of
tuples.
Subtraction attack is responsible for deleting all the attributes of the selected tuple, the targeted
tuple may carry the watermark bit [31, 79]. In bit flipping attack every attribute of a selected tuple is
targeted, which inverts their LSBs and causes loss of watermark information. Although, the tuple is
still there in the relation after bit flipping attack, however in case of deletion attack, whole tuple is
deleted. Figure 4.12 provides comparison of bit flipping, subtraction, and addition attack on fixed
attack level 10%, with varying fractions of tuple selected to be watermarked (0.01 to 0.64). Addition
attack is causing FPs, because size of the relation is increased by 10 percent. However, loss of few
watermarking bits is noticed after bit flipping and subtraction attack.
Figure 4.12 RBW-RD Capacity Comparison After 10% To 80% Bit Flipping Attack
0
1000
2000
3000
4000
5000
6000
7000
0.01 0.08 0.16 0.25 0.32 0.4 0.48 0.56 0.64
Det
ecte
d W
ater
mar
k
Fraction of tuple selected to be watermarked (1/ λ)
No Attack
10%
20%
30%
40%
50%
60%
70%
80%
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Figure 4.13 shows comparison at 80% bit flipping, addition, and subtractive attacks, at different
fraction of selected tuples for watermarking. It is evident that FP rate, due to addition attack increases
with the increase in fraction of selected tuple. Furthermore, huge loss in watermarking detection rate is
Figure 4.13 RBW-RD Capacity Comparison of Simple Relation And After 80%
Subtraction, Addition, & Bit flipping (50% Attributes Altered)
Figure 4.14 RBW-RD Capacity Comparison Of Simple Relation And After 80% Subtraction,
Addition, & Bit Flipping Attacked
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
0.01 0.08 0.16 0.25 0.32 0.4 0.48 0.56 0.64
Det
ecte
d W
ater
mar
k
Fraction of tuple selected to be watermarked (1/ λ)
No Attack
80 % Subtraction
80 % Addition
80 % Bit Flipping(50%)
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
0.01 0.08 0.16 0.25 0.32 0.4 0.48 0.56 0.64
Det
ecte
d W
ater
mar
k
Fraction of tuple selected to be watermarked (1/ λ)
No Attack
80 % Subtraction
80 % Addition
80 % Bit Flipping(100%)
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observed, in case of bit flipping and subtraction attack. However, difference between detection rate
after bit flipping and subtractive attacks is not prominent. Huge loss in the detection rate is noticed
because more tuples are deleted/altered as the fraction of selected tuple for watermarking increases.
Figure 4.13 illustrate that the effect of bit flipping (on 100% attributes) and subtraction attack on
watermark detection is nearly the same. Whereas, Figure 4.14 show that bit flipping attack (on 50%
attributes) is causing less effect on watermark detection compared to subtraction attack. Instead of
selecting all attributes for bit flipping attack, LSB of only 50 % (half) of attributes are changed.
Whereas, rests of the 50 % attributes are left unaltered.
4.4 Comparison of Proposed RBW-RD Technique with DEW
Technique
Comparison of proposed RBW-RD technique with DEW technique [22, 34, 35, 38] is provided in
current section. It has been studied that, mathematical complexity for DEW technique is higher
compared to RCM technique, whereas the embedding rate is identical [51]. The proposed RBW-RD
technique has high capacity because it uses all three steps for watermarking compared to only one step
of DEW technique. Whereas, only one step of proposed RBW-RD technique is causing high
distortion, thus proposed technique has less watermarking distortion.
Moreover, DEW technique with high value of DT check results in more FPs [22] however, no FPs
Figure 4.15 DEW Method Comparison After Subtraction, Bit Flipping, Addition
Attack, & No Attack On Relation (Average Of 10, 20…90% Attack)
0
500
1000
1500
2000
2500
0.01 0.02 0.04 0.08 0.17
Det
ecte
d W
ater
mar
k
Fraction of tuple selected to be watermarked (1/ λ)
50 % Subtraction
50 % Biit Flipping
50 % Addition
No Attack
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are detected in the proposed RBW-RD technique, even if the value of DT check is high. Addition
attack may reduce performance of FP detection in both DEW and the proposed RBW-RD techniques
but the proposed technique will get slightly higher rate of FP because of high embedding success.
Thus, the proposed RBW-RD technique achieves high capacity as compared to DEW, while distortion
and complexity are less. Likewise, FPs in the proposed RBW-RD technique are less even if the value
of DT check is high.
4.4.1 Experimental Analysis of The Proposed RBW-RD Technique against DEW Technique
Results of the proposed RBW-RD and DEW techniques are provided in Figure 4.15 and 16. Both
figures show 50 % subtraction, bit flipping, addition, and no attack on RW. Values of DT check is kept
high for both DEW (Figure 4.15) and the proposed RBW-RD technique (Figure 4.15). Average of nine
attack levels (10, 20 … to, 90 % attack) is taken as one point. In both figures, five points (0.01, 0.02,
0.04, 0.08, and 0.17) are selected for watermarking fraction. Watermarking capacity is low at point
0.01, while watermarking capacity is high at 0.17.
Increase in capacity is noticed, with the increase in fraction of tuples selected to be watermarked, as
a result difference between results of all attacks increases. Results show that watermark embedding
and extraction rate in the proposed RBW-RD technique is high compared to DEW technique. Further,
at high value of DT check, DEW technique has high probability of FPs detection. Despite of FP
Figure 4.16 RBW-RD And DEW Method Comparison After Bit Flipping
Attack (After Taking Average Of 0.01, 0.02, 0.04, 0.08, And 0.17)
325
375
425
475
525
575
625
675
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.79 0.89
Det
ecte
d W
ater
mar
k
Fraction of tuple attacked (1/ λ)
RBW-RD Bit Flipping
DEW Bit Flipping
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detection in DEW technique, the watermarking capacity of the proposed RBW-RD technique is high.
Figure 4.17 provides comparison between detection results of DEW and the proposed RBW-RD
techniques. In this figure bit flipping attacks on 0.00 to 0.89 fraction of tuple are performed. However,
for watermark embedding and detection overall 0.064103 fraction of tuple are used. Average of five
fractions (0.01, 0.02, 0.04, 0.08, and 0.17) is taken as one point. The proposed RBW-RD and DEW
techniques are both equipped with DT and bit checking capabilities as well as have same procedure for
Figure 4.17 RBW-RD Comparison After Subtraction, RBF, Addition Attack, &
No Attack On Relation (Average of 10, 20…90% Attack)
Figure 4.18 RBW-RD And DEW Method Comparison After Addition Attack
(After Taking Average Of 0.01, 0.02, 0.04, 0.08, And 0.17)
0
500
1000
1500
2000
2500
0.01 0.02 0.04 0.08 0.17
Det
ecte
d W
ater
mar
k
Fraction of tuple selected to be watermarked (1/ λ)
50 % Subtraction
50 % Biit Flipping
50 % Addition
No Attack
0
200
400
600
800
1000
1200
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.79 0.89
Det
ecte
d W
ater
mar
k
Fraction of tuple attacked (1/ λ)
RBW-RD Addition
DEW Addition
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tuple and attribute selection. Therefore, it is noticed that effect of bit flipping and addition attacks in
both techniques is almost same, because the value of DT check is kept high. However, sorting attack
does not create any disturbance in process of watermark detection, because both techniques are using
same method for tuple and attribute selection [47].
From Figure 4.17, it is evident that after bit flipping at different attack levels, the proposed RBW-
RD technique attains better detection rate compared to DEW technique. Whereas, Figure 4.18 shows
that the addition attack results of the proposed RBW-RD technique are consistently high compared to
DEW technique at all levels. Watermark detection rate after bit flipping attack consistently decreases,
as the fraction of attacked tuple increases. Conversely, detection rate after addition attack is
consistently increasing because of increase in FPs.
Figure 4.19 shows capacity comparison of DEW technique on different value of DT check. It is
illustrated that for DEW technique watermarking capacity is changing with change in value of DT
check. However, results of the proposed RBW-RD technique will remain high for all values of DT
check, as discussed in section 4.2. Consequently, results show that the proposed RBW-RD technique
Figure 4.19 Capacity Of DEW Method After Changing Values Of DT
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will have high capacity compared to DEW technique, even at low value of DT check
Std for RW is calculated, using the proposed RBW-RD technique on fixed (high) value of DT check
(0-999). Whereas, std for RW is calculated, using DEW technique by consistently changing value of
DT check. Difference between std (distortion) of RW is calculated for both DEW and the proposed
RBW-RD techniques by using equation (4.6). Difference represents improvement of proposed RBW-
RD technique over DEW technique. Figure 4.20 shows improvement in std (distortion) of proposed
RBW-RD technique over DEW technique. Improvement in distortion is calculated for varying value
of DT check for DEW technique while value of DT check of the proposed RBW-RD technique is kept
constant (0-999).
4.5 Chapter Summary
Results and analysis show that the proposed RBW-RD technique is robust, blind and reversible for
relational database watermarking. Comparative analysis of the proposed RBW-RD technique with
RCM and DEW techniques show noticeable improvement. The proposed watermarking technique has
Figure 4.20 Decrease In Distortion By Using RBW-RD (Fixed DT) Over
DEW (Changing DT).
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achieved maximum capacity by utilizing both types of attribute pairs that belong to or do not belong to
RCM domain. Further, no overhead of adding side information is required as well as embedding
distortion is minimized by utilizing distortion tolerance parameter. Additionally, the utilization of
effective automatic bit checking has enabled the proposed technique to increase watermark security
and reduce FP rate. However, both integer and fraction portion of the numeric attribute are utilized for
watermark embedding. Therefore, if fraction portion is attacked even then the watermark can be
restored exactly.
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WATERMARKING OF DNA Chapter 5SEQUENCES
Rapid increase in the amount of information is noticed in current digital era. As a result need for larger
data storage devices with low physical size have increased. Likewise, storing huge information using
data hiding approaches gives extra advantage of increased security. That can facilitate data storage as
well as providing data authenticity and copyright protection. Researchers have recently exploited DNA
medium both for data storage as well as for secret information hiding through different data hiding
techniques [25, 80]. DNA is an organic compound and hereditary information for all living organisms
is present in it. DNA components are composed of strands of four nucleotides that are Guanine (G),
Adenine (A), Thiamine (T), and Cytosine (C).
Chemical and biological rules help to understand formation of DNA nucleotides [81-83]. This
information can be useful for embedding watermark information in DNA medium [84]. High DNA
Length of microscopic organisms like bacteria can help to store big amount of information. Watermark
can be embedded as well as extracted from DNA medium that strengthens the idea of biological
storage devices [85]. DNA watermarking can be helpful for copyright protection of Genetically
Modified Organisms (GMOs), which helps their unlawful usage [86].
The proposed synonymous substitution based watermarking for DNA sequences (SSW-DNA)
attains high capacity and achieves robustness against mutations. It exploits whole coding region as a
result high data storage is attained. Existing DNA watermarking systems use only 4-fold synonymous
codons, which may not increase watermarking capacity, as 2-fold and 3-fold synonymous codons
makes substantial portion of DNA. The proposed method facilitates the use of 4-fold, 3-fold, and 2-
fold synonymous codons, enabling high data storage capabilities. Structural information is retained
using binary strings and watermark is encoded before embedding using Reed Solomon (RS) codes.
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Additionally, the biologically synonymous substitution method has advantage of sustaining the amino
acid sequence, thus the DNA functionality is retained.
5.1 Sequences Used for Testing
Different publicly available databases for DNA are available over the internet. The proposed SSW-
DNA method is applied on the DNA sequences shown in Table 5.1. The details of DNA sequences are
obtained from (NCBI) National Centre for Biotechnology Information database [87]. The NC_012806
is the GENBANKID of yeast mitochondria DNA sequence, while the remaining DNA sequences are
members of the Pisces and Amphibian family [87].
5.2 The Proposed SSW-DNA Method
The presented SSW-DNA watermarking technique consists of a data embedding and a data extraction
module as shown in Figure 5.1. The watermarking data is first converted to binary format and then
encoded using RS coder. Then the synonymous substitution technique is used for embedding
watermark in to the DNA sequence. Binary string is used to align the DNA sequence in the extraction
module. The watermarked data is then obtained using synonymous substitution method. The obtained
data is passed through RS coder for remove the invalid bits. Lastly, the data is converted from binary
to the original format.
Table 5.1: Dataset
GENEBANK ID Length
NC_012806 626
AB571609 1130
AB571626 714
JQ070418 2461
JQ085958 1070
JQ268556 1796
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5.2.1 Data Embedding Section
The watermark data and DNA is provided in alphanumeric character to the embedding module. The
embedding process ensures that the functionality of host DNA is not disturbed. Embedding module
converts the watermark data in to binary format and applies RS codes to provide protection to the data.
The RS code is capable of correcting multiple burst bit errors. Thus, the lost watermarked data bits can
be recovered later, on the detection side.
Figure 5.1 SSW-DNA Method
The data is then embedded in the DNA sequence using synonymous substitution method. The data
is encoded using nucleotide sequence according to set of biological rules given in Table 5.2. It shows
that the specific pair of bits is translated to the corresponding nucleotide base. Every single nucleotide
base is represented by a pair of bit, for example: 11 correspond to T, 10 correspond to G, 01 represents
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C, and 00 represents A. Therefore, a nucleotide sequence for example CATG can be represented as
„01001110‟ in binary format.
5.2.2 Correction of Errors
Mutation naturally occurs constantly in cells of every living organisms [88-90]. Mutation occurs inside
the DNA medium and can change its structural organization and composition. Mutations are of
multiple types subject to multiple factors [91]. Mutations can affect the watermark information stored
inside the DNA medium. One of a type of mutation is point mutation that can alter the arrangement of
DNA sequence. Another type of mutation is non-sense mutation, which cause distortion in the reading
frames of DNA.
This distortion can disturb the watermark information because; watermark can be embedded in
coding region of the DNA sequence only. Different other type of mutations may also cause problems
for exact detection of watermark, for example missense and some variations of point mutation. A two
layer methodology is utilized in the proposed technique which helps to reduce the data losses due to
mutation. This two layer methodology consists of RS codes and a sequence alignment strategy.
5.2.3 Employing RS Codes for Restoring Mutation Losses
The error correction scheme adopted by proposed SSW-DNA technique involves RS codes, to solve
the problem of data loss because of mutations. RS codes are block based linear codes and provide
good performance in the recovery of watermark bits [92]. RS codes are used extensively in digital
communication for error correction. In the presented method, RS code is applied upon the watermark
data, which encapsulates some parity bits in to the watermark data. At the extraction side the parity
Table 5.2: Data Encoding Table
Binary Sequence Base
11 T
10 G
01 C
00 A
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bits are useful for exact recovery of watermark data. Pictorial representation of using RS coder in
proposed method is shown in Figure 5.2.
Sender Side
Reciever Side In-v
ivo
RS EncoderUser
watermark
Extraction
Mutation
RS Decoder
Embedding
User
watermark
Figure 5.2 RS Code Implementation
Data is represented using k bits, whereas n-k shows the number of parity bits. Both n and k are
combined by RS coder and final watermark data is represented as n. Over all pictorial detail of the data
bits is shown in Figure 5.3.
Data bits Parity Bits
n bits
k bits n-k bits
Figure 5.3 Structure of Text Encoded Using RS Coder
The ability of the error correction using RS coder depends upon the ratio n/k. Synonymous
substitution technique is used for embedding watermark data in the DNA sequence. During watermark
extraction process the RS coder rectifies the erroneous bits in the watermarked data. The parity bits (n-
k) embedded along the watermark data, helps to correct the multiple bits flipping occurred in the
watermark data.
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5.2.4 Enhanced Synonymous Substitution Technique
Main focus of the proposed approach is to sustain the normal functionality of DNA after the data is
embedded. The synonymous substitution method fulfills the above purpose. Synonymous substitution
also referred as silent mutation is responsible for altering the DNA sequences using a specific
phenomenon, which does not affect any of the activities of the cells of living organisms. For example,
the generation of amino acids is not affected by embedding watermark using synonymous substitution
and thus protein synthesis process of DNA is not interrupted. A DNA sequence consists of coding and
non-coding regions. Only coding region of the DNA is used for watermarking and non-coding region
is left unused because effect of altering non-coding region is not studied yet.
DNA sequence
Sequence end
Get next codon
EndYes
No
2 or 4 fold
codon
Convert 2
watermark bits
to nucleotide
Insert to Least
significant base
Replace the
original codon
Select 1st
synonymous
codon if bit=0
else 2nd codon
Replace original
codon
If 2/3 fold
If 4 fold
Figure 5.4 Synonymous Substitution
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During the process of protein synthesis the coding region of DNA translate into a specific chain of
amino acid. Three successive nucleotides known as a degenerative codon is translated in to an amino
acid, following a specific genetic rule. Different patterns of successive nucleotides can be translated to
the same amino acid. This multiplicity feature of amino acid is named as number of fold for
degenerative codons. All the successive nucleotides (number of folds) that are translated in to same
amino acid are known as degenerative codons. For example, both ATG and ATA translate to MET
amino acid. Thus, MET amino acid has 2 fold degenerative codons.
According to degenerative codons the coding region can be divided into three types; 4 fold, 3 fold,
and 2 fold degenerative codons. Four fold codons can be translated into the same amino acid for
example TCA, TCG, TCT, and TCC all translate in to “Ser” amino acid. AUU, AUC, and AUA present
the example of three fold codons that are translated into “isoleucine” amino acid. AGG and AGA is the
example of two fold codons that are translated into “Cys” amino acid. The proposed SSW-DNA
approach utilizes all three types of degenerative codons for hiding watermark information. Figure 5.4
provides flowchart of the synonymous substitution process. The process of embedding watermark
information in the degenerative codons is described in the coming section.
5.2.4.1 Degenerative 4-fold Codons
In the proposed approach all three types of degenerative codons are used and in all types the third
nucleotide is used for watermarking. In Figure 5.5, 4 fold degenerative codons is elaborated using an
example. CTT is the available codon of the DNA for watermarking. CTT and other three relevant
synonymous codons (CTC, CTA, and CTG) translate into same amino acid Leucine (LEU). Following
Table 5.3: Synonymous Substitution
Bit Amino acid 00 01 10 11
Theorine (Teu/T) ACT ACG ACC ACA
Valine (Val/V) GUT GUG GUC GUA
Alanine (Ala/A) GCT GCG GCC GCA
Arginine (Arg/R) CGT CGG CGC CGA
Glycine (Gly/G) GGT GGG GGC GGA
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the watermark information, third nucleotide T (‟11‟) is replaced with nucleotide G („10‟). Thus, after
watermarking the codon CTT will be converted to codon CTG. This substitution of third nucleotide
helps transformation among degenerative codons without alarming the translation to the relevant
amino acid. Few amino acids with the variation in their 4 fold degenerative codons are shown in Table
5.3. Using watermark information the suitable synonymous codons are selected from the available
possible codons.
Leucine
(LEU/L)
CTT
CTC
CTA
CTG
CTT
G
CTG
Figure 5.5 Data Insertion In 4-Fold Degenerative Codons
5.2.4.2 Degenerative 2-fold and 3-fold Codons
Presented approach can utilize both 3 fold and 2 fold degenerative codons for embedding watermark
information. Synonymous substitution capability is maintained and one bit of watermark information
is embedded for one codon (representation of amino acid sequence). For both 3 fold and 2 fold
degenerative codons following steps are followed to store a single watermarking bit.
1. First synonymous codon is used and the second synonymous codon is left unused if the
watermark bit is „0‟.
2. Second synonymous codon is used and the first synonymous codon is left unused if the
watermark bit is „1‟.
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3. Whereas in case of 3 fold degenerative codon the third synonymous codon is left unused
throughout.
Histidine
(HIS/H)
CAT
CACCAT
1
CAC
Figure 5.6 Data Insertion In 2-Fold Degenerative Codons
An example of 2 fold degenerative codon is provided in Figure 5.6. CAT is the synonymous codon
that translates to HIS amino acid [80] according to the standard genetic code. If the watermark bit to
be embedded is „1‟ then the second synonymous codon is used for watermarking. Thus, CAC
synonymous codon replaces CAT synonymous codon.
Three (3) fold degenerative codons occur rarely in the DNA. However, the „isoleucine‟ amino acid
represents an example of 3 fold degenerative codon that is represented by three synonymous codons
(AUU, AUC, and AUA). If the watermark bit is „1‟ first synonymous codon AUU is used. However, if
the watermark bit is „0‟ then the second synonymous codon AUC is used. Whereas, the third
synonymous codon AUA is not taken into account. After embedding the message into host DNA
sequence, a watermarked sequence is obtained. Figure 5.7 provides pictorial representation of the
embedding module.
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Figure 5.7 Data Embedding Module
5.2.5 Data extraction Section
High structural mutation rate on DNA can cause problem in the recovery of watermark information.
Therefore, sequence alignment of the DNA is an important measure during the data extraction process,
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when the mutation rate is high. However, only RS code based error correction technique alone can be
useful under normal circumstances (no mutation).
5.2.5.1 Sequence Alignment Step to Tackle Mutations
Different techniques have been developed to transport DNA sequences over communication channel
e.g. Wavelet Transform, Fourier Transform, etc.[93, 94]. On the detection side, binary strings are
utilized for setting the alignment of watermarked DNA [94]. Alignment of sequences helps to remove
physical disorders caused by mutations. Watermark data can be affected by the occurrence of
mutations. Sequence alignment in the proposed SSS-DNA method is used for transferring the
watermarked DNA sequence information (as side information) to the data extraction section. In this
regards, the changes caused by mutations are revoked using the transferred watermarked DNA
sequence information (transferred as side information). As we know that the side information is
transferred as binary strings therefore LZWA technique is used for encryption, it provides good
compression ratio and high speed conversion [95]. To determine the size of the side information an
example is provided. If a DNA sequence composed of 1000 nucleobases is watermarked, then three
binary strings each of length 1000 will be used for passing side information. Where, LZWA can be
used to compress the side information at 25.4% compression ratio. Therefore, total 724 bits will be
needed to pass the side information of the DNA sequence which consists 1000 nucleobases [96].
5.2.5.2 Producing Binary Strings
Four binary strings are used to represent a DNA sequence; each string represents a separate nucleotide.
XG(n), XA(n), XT(n), and XC(n) are the binary string representing nucleotide G, A, T, and C, one-to-
Table 5.4: VOSS Representation Of DNA Sequence
X(n) A T G C G A T C A T G A C C T G C A
XA(n) 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 1
XG(n) 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0
XC(n) 0 0 0 1 0 0 0 1 0 0 0 0 1 1 0 0 1 0
XT(n) 0 1 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0
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one. Length of a single binary string is same as the length of DNA sequence. Table 5.4 is showing an
example of a DNA sequence “ATGCGATCATGACCTGCA”. In the binary sequence „1‟ donates the
occurrence of nucleotide, where „0‟ represents the occurrence of nucleotide in other binary sequence.
The binary representation of the DNA sequences helps in reducing the distortion caused by mutation.
In this connection, out of four binary strings three are transferred as side information to the extraction
section. These binary strings are used for aligning the sequence; to recover the changes introduced by
mutations in DNA medium.
5.2.5.3 Transfer Channel
Binary strings can be compressed using LZMA or Run-Length Coding [97] and can be transferred on
a separate channel to the detection side. At the detection side, original DNA sequence is reassembled
using binary strings (alignment information). Only three of these four binary strings needs to be
transferred over the communication channel. The reconstructed DNA can remove any kind of
structural defects occurred due to mutations in DNA sequence. Original DNA sequence can be
reconstructed using only three binary strings. Figure 5.8 provides an example of three binary strings
XC=000010000110000, XA=100000110000010, and XG=011001000001000. Figure 5.8
demonstrations the reconstructed DNA sequence using the three alignment information.
Figure 5.8 Reconstruction Of DNA Using Binary Strings
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5.2.5.4 Mechanism of Extracting Data
Process of extrication of watermark data from the DNA sequence is explained in current section. In
case of mutations, the DNA sequence is aligned using binary strings. Sequence alignment helps to
restore the alterations occurred inside the DNA, resulted because of mutations. The reconstructed
DNA is passed to the extraction module. Different stages of the extraction side are given in Figure 5.9.
Watermark
extraction
Start
Sequence
Alignment
If sequence
end
Get each codon
Sequence
Alignment
Information
Load
Watermarked
DNA
Decode
watermark data
Extracted
Watermark
Codon type
Extract 3rd
baseDecode
watermark bit
Convert to
binary
DNA-Binary
mapping table
Extracted
data
No
4-fold codon
2-fold codon
Yes
End
Figure 5.9 Data Extraction Module
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Coding region of the DNA is used for the extraction of watermark using following method.
1. Least significant base from the codon is obtained in case of 4 fold degenerative codon.
2. Whereas only one bit is obtained as watermark information in case of 2 fold or 3 fold
degenerative codon.
I. If the current codon is first of the degenerative codons then „0‟ is obtained as watermark bit.
II. If the current codon is second of the degenerative codons then „1‟ is obtained as watermark
bit.
Table 5.2 is used to convert the nucleotide (base) to binary strings, as the table shows that each
nucleotide translates to two bits. Whereas signal bits are obtained in case of 2 fold and 3 fold
degenerative codons. The resultant watermark information obtained from the DNA is passed to the RS
coder. Where the watermark data lost due to mutation is restored.
5.3 Results and Analysis
The proposed DNA-HSS technique is evaluated by using both biological DNA sequences and
synthetic DNA sequences. Table 5.1 provides the list of biological DNA sequences used for evaluation
whereas; the synthetic DNA sequences are generated randomly. Results of the experiments show that
presented technique does not disturb the core functionality of DNA as well as provides high data
storage capacity compared to existing methods.
5.3.1 Capacity of Storing Bits
Capability of the proposed model in terms of data hiding capacity is given in Table 5.5. First column
of the table show Locus of the DNA whereas, Length (L) column of the table show the total number of
nucleotides. UCa
shows the number of codons within coding region of DNA used for storing
watermark information. While, UnCa
represent the number of codons in noncoding region, and this
portion of the DNA is left unwatermarked. Total number of bits stored in a DNA sequence is shown
by bs (Bit stored). [69]. It is clear from our technique that 4 fold degenerative codons can store two
bits, while 3 or 2 fold codons can store one bit only. Therefore, the mathematical representation to
estimate the number of bits stored inside the DNA sequence is provided in equation (5.1).
) , ) ) (5.1)
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In the equation above, N4f represents the number of 4 fold degenerative codons and N(2,3)f shows
number of 2 fold and 3 fold codons that only belong to the coding regions of DNA. Capacity of a
DNA sequence of storing a watermark is determined by bpn (bit per nucleotide), as explained through
equation (5.2).
𝑝𝑛 𝑏𝑠
𝐿 (5.2)
With increase in length of coding region the capacity of storing watermark also increases, which is
apparent from Table 5.5. Comparatively, a lengthy coding region of AB571609 has high bpn value as
compared to others less lengthy coding regions for example JQ08595.
5.3.2 RS Codes for Error Correction
Losses due to mutation are handled using RS codes and the binary string representation. Different
artificial mutations are applied on the watermarked DNA and the error detection and correction
methods are used to analyze their performance. Binary string representation removes structural
changes in the DNA by realigning the sequence at the receiving end. In order to successfully restore
the losses occurred on communication channel RS coder is utilized. Numerous scenarios of mutations
can arise, subject to the occurrence and density of these mutations. RS coder has been tested in most of
these different scenarios. Result of applying RS coder over burst and point mutation is provided in
Figure 5.10. RS coder is evaluated using total mutated bits versus mutation bits left uncorrected.
Table 5.5: Bit Storage Capacity
Locus Length (L) UCa UnC
a bs bpn
NC_012806 626 164 44 236 0.371
AB571609 1130 313 63 489 0.433
AB571626 714 95 143 140 0.196
JQ070418 2461 683 137 1040 0.423
JQ08595 1070 101 255 160 0.150
JQ268556 1796 142 456 217 0.121
a: 1 Codon= 3 Nucleotide
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Figure 5.10 RS Coder Performance For Point And Burst Mutation Scenario
Results of two mutations (point and burst) are plotted in the Figure 5.10. In case of burst mutation,
when the occurrence of mutation is low, the erroneous bits are completely restored. However, when
the occurrence of mutation exceeds 42, the success rate of recovery of erroneous bits is disturbed. The
point mutation comparatively better results as compared to burst mutation. The success ratio of
recovery of erroneous bits is affected, after the occurrence of mutation exceeds 90. The slope for the
decreasing performance in case of point mutation is considerably lesser than that of burst mutation.
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Figure 5.11 RS Coder Performance In Random Mutation Scenario
Figure 5.11 provides comprehensive insight in to the use of RS coder in case of burst mutation.
Data is embedded in the DNA sequence and mutations are applied on the watermarked DNA sequence.
Correctly recovered bits, erroneous bits, total data bits, and error correction rate is plotted in Figure
5.11. In order to make it more clear, the error correction rate has been calculated and is showing the
percentage of correction performed by the RS coder. Results show that success rate of correcting
erroneous bits is 100% in majority of the cases, while only few are showing little decrease in success
rate. Figure 5.12 show the results obtained by applying RS coder in case of random mutations
scenario, where the success rate is less compared to the burst mutation.
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Figure 5.12 RS Coder Performance In Point Mutation Scenario
5.4 Comparison with Existing Methods
Comparison between the proposed SSW-DNA method and existing methods for storing data in DNA
medium is presented in Table 5.6. In terms of data storage SSW-DNA method provides high storage
capacity as compared to other techniques. In addition to the utilization of 4 fold degenerative codons, the
proposed method is completely utilizing the data storage capacity, by using 2 fold and 3 fold
degenerative codons as well. Comparison between different DNA sequences is plotted in Figure 5.13
against the bpn value.
It is evident from the graph that the proposed method is better compared to existing methods in terms
of data hiding capability. Figure 5.13 indicates roughly 33 % average increase of the proposed SSW-
DNA method over other techniques.
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Figure 5.13 Bpn Comparison
Figure 5.14 shows results of RS coder for error correction on several block sizes (8, 10, 12, and 14).
It is evident that the mutation correction capability of RS coder decreases with the increase in block
size. The average uncorrected mutations are high at block size 14, whereas low at other block. It can
be concluded that the block size affects the ability of restoring erroneous bits.
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
ypt7 AB571609 AB571626 JQ070418 JQ085958 JQ268556
bp
n
Locus
Shimanovsky et al.
Arital et al.
DNA-Crypt
SSW-DNA
Table 5.6: Comparison With Existing Techniques For Data Hiding Capacity
Locus Length Shimanovs
kyet al. [98]
Aritaet
al. [63]
Heider
et al. [64]
SSW-
DNA
NC_012806 626 167 164 144 236
AB571609 1130 309 313 352 489
AB571626 714 87 95 90 140
JQ070418 2461 667 683 714 1040
JQ085958 1070 103 101 118 160
JQ268556 1796 132 142 150 217
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Figure 5.14 Average Uncorrected Mutations At Different Block Sizes
The average uncorrected mutation with changing n/k ratio and increasing mutation count is shown
in Figure 5.15. As mentioned earlier, by changing n/k ratio the error correction capability of RS coder
is also affected. Smaller n/k ratio reduces the number of redundant bits, which results in reducing the
recovery of data bits from erroneous bits. Whereas, the higher n/k ratio helps in better recovery of data
bits even after the high rate of mutations. From Figure 5.15 it can be observed that at the same
mutation count, RS coder with lower n/k ratio will cause more uncorrected mutations. Whereas,
number of mutations left uncorrected at n/k 3 is very low. Therefore, the best result of successful
data retrieval is provided by n/k 3.
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Figure 5.15 Average Uncorrected Mutation Trend At Different N/K
RS code is very useful in restoring the lost watermark bits when the channel nose is high, for example
missense and frame shift mutations. These codes are very effective in error correction competency, and
they provide high probability to correct any erroneous bits present in the data. Different experiments
performed using RS codes have shown that error rectification ability increases with high n/k ratio and
smaller block size.
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5.5 Chapter Summary
The presented SSW-DNA watermarking technique is useful for different applications, including
advancement of biological data storage devices and copyright protection of GMO‟s. The proposed
SSW-DNA technique is capable of confidentially storing information in DNA without causing any
interference in its properties. In order to store data without interfering the sole functionality of DNA,
synonymous substitution based watermarking technique is used.
Currently, the synonymous substitution techniques only use 4-fold codons for watermarking DNA
sequences. Whereas, 2-fold and 3-fold synonymous codons form a substantial portion of DNA
sequence therefore, proposed SSW-DNA method utilizes 4-fold, 3-fold, and 2-fold synonymous
condones to increase the data storage capacity. Additionally, the proposed SSW-DNA technique is able
to recover losses occurred due to mutations by using structural integrity of the DNA.
In order to analyze data storage and error correction ability of the proposed SSW-DNA method
different experiments are performed. These results show that the proposed technique can store more
information in DNA, compared to different methods. Changing mutation rates and different
parameters like n/k ratio and block size are used to compare the proposed approach with underlying
watermarking/data hiding techniques. Thus, the proposed SSW-DNA approach is capable of storing
data in DNA without causing any threat to the survival of the living organism. As a result, the data is
safely stored inside the living organism and the organism can sustain the stored information over
period of numerous generations.
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CONCLUSIONS AND Chapter 6FUTURE DIRECTIONS
6.1 Thesis Summary
Focus of the current study is on developing capable constraint centered watermarking systems for
digital multimedia objects belonging to relational databases, and DNA of the living organisms.
Throughout the progress of this PhD, a number of watermarking necessities such as payload,
reversibility, false positive rate, blindness, imperceptibility, distortion, cost, and usability were
identified. With larger understanding of these necessities, we were able to construct more sufficient
and operational watermarking systems.
GA is used to improve the capacity of DEW method in databases, while keeping distortion
tolerance fixed. GA introduces some randomness in DEW technique, thus making it difficult for the
attacker to predict attributes. Security of the watermarking system is also enhanced by reducing the
distortion and minimizing the abrupt changes caused by DEW method. This is achieved by two
measures added in the fitness function of GA, first by using the knowledge of the neighborhood values
of the relational database, second by minimizing the distortion introduced by selecting attributes
resulting in minimum distortion. Results are showing improvement in terms of embedding capacity as
well.
Consequently, more watermark bits can be embedded in the database, while distortion introduced
in it is minimalized. This provides more comfort for the user and leaves fewer options for the attacker
to destroy the watermark. Detection technique of GADEW method resolves problem of reshuffling
attacks on attributes. It is robust against addition, deletion, sorting, bit flipping, tuple and attribute-
wise-multifaceted, and additive attacks. It has also solved problem of false positive rate at detection
side. However, the proposed GADEW is a semi-blind watermarking technique that requires the GA
chromosome information at the detection side. Additionally, the embedding phase of the proposed
GADEW method requires more time as compared to existing methods, because GA is computationally
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intensive. In future, we intend to develop a reversible watermarking technique, which can handles both
integer and floating point values present in a single relation.
A new robust, reversible, and blind watermarking method for relational database is proposed
(RBW-RD), which exploits reversible contrast mapping technique. Proposed technique achieves
high watermarking capacity, because watermark bits are successfully embedded even if the
transformed pairs do not belong to RCM domain. Additionally, there is no need for extra storage,
which is another factor of increase in embedding watermark. On the other hand, DT factor is added for
providing extra control to the proposed RBW-RD technique. It helps to observe the limitations for
each attribute so that transformed value may not exceed their DT level. At detection side,
automatically generated watermark bits are matched with extracted watermark bits. As a result, FP‟s
caused by addition or bit flipping attacks are minimized.
Proposed technique utilizes all three steps of watermarking. Whereas, pre-existed properties of
RCM are also retained, that is, no compression or encryption is required and the computational
complexity is also less. Additionally, more capacity is achieved with less distortion and FPs, without
storing extra bits. While both integer and fraction portion of the numeric attribute are utilized for
watermark insertion. Proposed technique can tackle the sorting attack inherently while experiments
have shown robustness against the bit flipping and addition attack as well. As the proposed RBW-RD
technique utilizes third step of RCM by exploiting both integer and fraction portion of targeted
attribute. Thus, the proposed approach is more suitable for relations containing fraction values.
Furthermore, utilizing third step of RCM technique creates 50 % chance of losing LSB‟s of only
fraction portion of the restored attributes. Finally, it is also compared with DEW based watermarking
technique. Comparison shows superiority of the proposed RBW-RD technique over DEW technique in
terms of capacity, distortion, and false positive rate.
An interesting technique for DNA medium is presented (SSW-DNA) that secretly stores the data
inside DNA without disturbing its role of carrying hereditary information. Biologically, synonymous
substitution method maintains the amino acid sequence, thus DNA functionality is retained. The
proposed SSW-DNA method combines regions comprising of 2-fold and 3-fold codons along with 4-
fold codons for watermarking. Thus, the data hiding capacity of the proposed technique is high.
Similarly, two-layered error correction scheme is incorporated in the proposed SSW-DNA technique.
Structural information is retained using binary strings and watermark is encoded before embedding
using Reed Solomon codes.
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Different tests are carried out for performance analysis of the proposed SSW-DNA method
regarding the data storage capacity and error correction capability. Experimental results show that the
proposed method is able to store more data in DNA, compared to the existing techniques. The dual
layer mutation correction approach has been tested for different mutation rates. Different parameters
including block size, n/k ratio are used to obtain performance curves. Results show that the proposed
technique offers better performance in terms of recovering data losses due to mutations. However, in
order to reduce mutation losses, the detection module requires binary strings for sequence alignment.
Therefore, the proposed SSW-DNA approach can be categorized as semi-blind watermarking
technique. Lengthy DNA of microscopic organisms such as bacteria can help to store large amount of
information. Thus, strengthens the idea of biological storage devices. DNA watermarking supports
copyright protection of Genetically Modified Organisms (GMOs).
6.2 Future Research Directions
6.2.1 Intelligent Watermarking
Intelligent techniques take time for embedding a watermark. In order to reduce the computational time
different ways needs to be adopted, along with sustaining the key advantages of the intelligent
approaches. In figure 3.6 and 3.7 it can be observed that for less number of tuples (small chromosome
size) the GA achieves high watermarking capacity but when the chromosome size is large, the
capacity of the proposed GADEW method decreases. The decrease in capacity is caused by the
selection of large number of tuples to be watermarked (large chromosome size). To reduce this problem,
multiple runs of GA might be helpful. Multiple runs may increase the time of watermark embedding
process but the improvement in capacity might remain the same. Combination of multiple intelligent
techniques can also be useful for attaining desired outcomes. There is high scope of exploring different
intelligent techniques to bring improvement in different properties of underlying watermarking system.
6.2.2 Reversible Watermarking
Development of new Reversible watermarking techniques for different multimedia objects and
improvement in the current reversible watermarking systems needs to be explored. There are number
of watermarking features that needs to be addressed. Reversible watermarking could be blind, semi-
blind, or non-blind. In this regard, blind watermarking is a technique with stringent conditions but is
considered a favorable one. Along with blindness and reversibility, the robustness property is highly
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desirable. Therefore, different aspects of attacking methods and their analysis can be made for the
existing reversible watermarking methods.
6.2.3 Watermarking Different Objects
Other potential multimedia objects may be targeted for bringing improvements in watermarking
features. These multimedia objects can be software, HTML documents, audio, video, Natural
language, etc. Reducing FP detection and distortion may be useful for certain type of multimedia
objects. Such objects do not tolerate permanent changes in their content, e.g. software and natural
language processing. However, high watermarking capacity and less distortion properties may be
interesting for objects like audio, video and HTML documents.
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