Arnold transform based Security Enhancement using Digital ...Arnold transform based Security Enhancement using Digital Image Watermarking with Complex Wavelet ... we have two trees

International Journal of Electronics Engineering Research.

ISSN 0975-6450 Volume 9, Number 5 (2017) pp. 677-693

© Research India Publications

http://www.ripublication.com

Arnold transform based Security Enhancement using

Digital Image Watermarking with Complex Wavelet

Transform

Sukhwinder Kaur,1,* and Dr. Rajneesh Talwar2, *

1 PhD Research Scholar, IKJPTU Jalandhar, India.

2 Principal, CGC Group of Colleges, Janjheri, Punjab, India.

Abstract

Digital image security is of utmost importance in today’s scenario. This paper

proposes a novel type of image security method involving Arnold

transformation and frequency domain transformation i.e. Dual Tree Complex

wavelet transform (DTCWT) .The main idea here is to decompose the cover

image into sub-bands using dual tree complex wavelet transform and then

applying Arnold Transform to the watermark image and again DTCWT is

applied to encrypted image. Finally watermarking rule is applied and

watermarked image is obtained. Here we are aiming at comparing the

performance of extracting the watermark from different sub planes of complex

wavelet transform which has not been done so far. A detailed analysis is

carried out to compare the performances against attacks in various subplanes.

The experimental results clearly show a better visual imperceptibility and an

excellent resiliency of the proposed watermarking scheme against various

types of attacks. The security is evaluated using a correlation coefficient and

PSNR values.

Keywords: Dual Tree Complex Wavelet Transform,Arnold Transform.

1. INTRODUCTION

Digital watermarking has gained a lot of importance in recent years as protection of

data i.e. may be images audio or videos . A robust watermark can resist all types of

attacks, while a fragile watermark gets destroyed even after the smallest

unintentional manipulation.

678 Sukhwinder Kaur and Dr. Rajneesh Talwar

Most of the image watermarking methods use Discrete Cosine Transform[2] (DCT)

or wavelet domain for detecting the tampered regions in the image. The original

image is partitioned into blocks, and selected block is embedded with its own

watermark using a secret key [5]. In [9] ridgelet transform as an efficient transform

for representing images with line singularities was proposed. Our proposed algorithm

aims on eliminating the drawbacks of such schemes, using a Dual Tree Complex

Wavelet Transform (CWT) based approach and Arnold Transformation. The DTCWT

is used to increase the robustness while encryption increases the security of the

watermark. From the simulation results it can be seen that our algorithm gives better

results in terms of PSNR ratio and along with that security of watermark is also

covered effectively. Proposed algorithm improves the quality of the watermarked

image, unlike conventional block-based methods.[7] The algorithm can work

effectively against geometrical attacks like rotation, Gaussian noise, salt and pepper

noise etc. The remainder of this paper is organized as follows. In Section 2 we present

the proposed complex wavelet transform scheme in the wavelet domain, and the basic

concept of Arnold Transformation. Section 3 contains the proposed block diagram for

watermark embedding and extraction .The results and performances of the proposed

scheme and conclusions are given in Section 4.

2. BACKGROUND

2.1 Basics of Wavelet Transform

Whenever DTCWT is applied to 2-D signals or images, it exhibits the following

features it is performed separably, using 2 trees for the rows of the image and 2 trees

for the columns of the image yielding a Quad-Tree structure with a 4:1 redundancy

[16].These 4 quad-tree components of each coefficient are combined by simple sum

and difference operations to yield a pair of complex coefficients. These are part of

two separate sub bands in adjacent quadrants of the 2-D spectrum. This produces 6

directionally selective sub bands at each level of the 2-D DT CWT. The DT CWT is

directionally selective because the complex filters can separate positive and negative

frequency components in 1-D, and hence separate adjacent quadrants of the 2-D

spectrum. Real separable filters are not able to implement this [16].That is why we get

better result for watermarking using DTCWT.Typical features of the Dual Tree

Complex Wavelet Transform (DT CWT) include good shift invariance that means

negligible aliasing. [1].Hence transfer function through each sub band is independent

of shift, and wavelet coefficients can be interpolated within each sub band,

independent of all other sub bands. Good directional selectivity in 2-D, 3-D etc. –

derives from analyticity in 1-D (ability to separate positive from negative

frequencies).Perfect reconstruction with short support filters is possible which is also

proved through the quality of the retrieved watermark [16].

Arnold transform based Security Enhancement using Digital Image Watermarking.. 679

2.2 DWT

Wavelets are the basis functions of a relatively new type of mathematical transform

that occupies a space between the spatial domain of image pixels, and the frequency

(Fourier) domain of spatial frequency components. Wavelet functions are scaled and

shifted versions of a common mother wavelet shape. In that sense they are simpler

than Fourier functions. Usually wavelets are scaled in generations: each parent having

4 children in 2-D, and each child being half the size (in length) of its parent.[3]The

main reason for using this transform is that we do not loose information[12] .

Wavelets are used mainly for analysis and synthesis of signals specially image

signals. As a signal consists of many frequencies wavelets can be applied for either

1D, 2D & 3D signals. [4]The main advantage being that it is a separable transform i.e.

it can be applied row wise and column wise, also called mathematical microscope as

more details are visible , by changing the scale of the picture so we can zoom to any

frequency with wavelets .In standard wavelet like DWT,only 3 directions i.e.

LL,LH,HH are available ,so to overcome this complex wavelet transform is used as in

complex wavelets, we have two trees one for real and another for imaginary .Each one

of them gives information in 3 directions ,so we get information in 6 directions.

If f(t) is the original signal ,then transform is given as

dt

Here the kernel function changes according to transform

dt

)

standard wavelet we are having one scaling function and and one wavelet

function Ψ(t) but in complex wavelets we are having two scaling functions and a pair

of wavelet functions [4]

This is called Hilbert pair [14] since they are having phase shift of 90°.Because of so

many directions characterization is done more efficiently in all the directions. So

complex wavelets are of great importance in watermarking as we need to preserve


information in all the directions effectively in applications like watermarking.As the

watermarked image [4]goes through the communication channel ,it can be affected

by attacks of various types of attacks during this process. So in order to retrieve the

original image or the watermark effectively DTCWT proves out to be quite beneficial.

As the DTCWT is an extension to standard wavelet & uses two separate trees of real

DWT filters that operate in parallel to generate real and imaginary parts of a complex

filter so we have double number of coefficients in DTCWT so more features can be

extracted. [11]When we implement watermarking using DTCWT ,the quality of the

watermarked image is much better than DWT which is visible through the value of

PSNR .When the watermarked image travels through the communication channel ,the

watermark can be retrieved back efficiently as compared to standard wavelet .To add

to security feature we have encoded watermark image using Arnold transformation

and then inserted it as a watermark to the cover image .To ensure level of security a

key is used while insertion and at the receiver side same key has to be given in order

to retrieve the watermark or the cover image.The results of this are much better as

compared to wavelet i.e. DWT in terms of PSNR and security .[8] So when we apply

DTCWT to image watermarking also we get six directionally selective subbands at

each level of the 2D DTCWT which provide us more information and hence during

watermarking when the image goes through the communication channel and has to be

retrieved again at the receiver end DTCWT gives better result as compared to

standard wavelets like DWT,DCT etc[4][17].

The Arnold transform has been used for encrypting the watermark image to provide a

feature of additional security.

Scrambling of the watermark ensures that the watermark cannot be extracted at the

receiver without the particular key which was used at the time of encryption.[10] This

adds more level of security to our algorithm Image scrambling technology depends on

data hiding technology which provides non-password security algorithm for

information hiding. The image after scrambling encryption algorithms is chaotic, so

attacker cannot decipher it [20].

3. PROPOSED WATERMARKING METHOD

In DWT one of the main limitations is that whenever the watermarked image

undergoes rotation ,watermark gets destroyed and cannot be retrieved at all but with

complex wavelet transform this problem is overcome & the watermark is effectively

retrieved. The DTCWT coefficients obtained from the first filter bank are called the

real part and the coefficients obtained from the other filter are called the imaginary

part. The real part of the image contains less important data compared to the

imaginary part which contains more information. Our algorithm embeds the

watermark into the whole image, that is, both the real and imaginary parts. This is

preferable because if an attack occurs while transmitting, it can be easily identified

While retrieving we can retrieve with the help of one of the subplanes. In other words,

if only the real part is used for watermark embedding and an unauthorized person tries


to extract the watermark, it can be done with much ease because the real part contains

less information and the sender would not even come to know about it. In the case of

using both real and imaginary parts, an attack can be easily identified as the imaginary

part contains more important information. If the filters in the upper and lower DWTs

are the same, then no advantage is gained. However, if the filters are designed is such

a way that the subband signals of the upper DWT can be interpreted as the real part of

a complex wavelet transform, and subband signals of the lower DWT can be

interpreted as the imaginary part. Equivalently, for specially designed sets of filters,

the wavelet associated with the upper DWT can be an approximate Hilbert transform

of the wavelet associated with the lower DWT. When designed in this way, the dual-

tree complex DWT is nearly shift-invariant, in contrast with the critically-sampled

DWT. Moreover, the dual-tree complex DWT can be used to implement 2D wavelet

transforms where each wavelet is oriented, which is especially useful for image

processing.The proposed watermarking scheme is based on Dual Tree Complex

Wavelet Transform approach. The watermark embedding and extraction algorithms

are described below.

3.1 Block Diagram

3.1.1 Watermark embedding algorithm:

In order to ensure both imperceptibility and the security of watermark Arnold

transformation is used for encrypting the watermark by providing a private key. This

will ensure that the watermark can only be retrieved on the other end by putting that

very key which was used during encryption.

(1) Take cover image C of size m*n and apply DTCWT to it to give the wavelet

coefficients.

(2) After that Arnold transformation is applied to the watermark image W ,to encrypt

the image with the help of a particular key.

=

Where N is the order of the digital image

(3)Now again Dual Tree Complex Wavelet transform is applied to the encrypted

image to get the relevant wavelet coefficients.

(4)In the next step watermarking rule can be applied to the two images namely cover

image and watermark image.

(5) Finally Inverse Dual Tree Complex Wavelet transform is applied to get the final

the final watermarked image WI.


DTCWT

Watermark

Image

Arnold Transform

DTCWT

Inverse DTCWTWatermarked

Image

Key

2nd Subplane

12th Subplane

1st Subplane

Cover Image

2nd Subplane

12th Subplane

1st Subplane

Apply watermarking rule

to the subabnd cofficients

Fig 1 Watermarking embedding algorithm using DTCWT with Arnold Transform

3.1.2 Watermark extraction algorithm

(1) Decompose the watermarked image WI into sub-bands using Dual Tree Complex

Wavelet Transform.

(2) From the coefficients obtained subtract the coefficients of cover image C ,we will

get the coefficients of the watermark image W’. This step is carried out for every

subplane separately.

(3) Apply inverse DTCWT to obtain the watermark image.

(4) Now apply inverse Arnold transform along with suitable key to get back get back

the decrypted watermark.


Watermarked Image DTCWT

Cover ImageDTCWT

Subtraction of cofficients

Inverse CWTEncrypted Water mark Inverse Arnold

TransformWatermark

Key

Selection of coff.

Of a particular

subabnd

Selection of coff.

Of a particular

subabnd

Fig 2 Watermark Extraction algorithm using DTCWT with Arnold Transform

4. EXPERIMENTAL RESULTS

When DWT is used for watermarking, it has many limitations.DWT replaces

infinitely oscillating basis functions of the fourier transform with a set of locally

oscillating basis function called wavelet. Any finite energy analog signal x(t) can be

decomposed in terms of wavelet & scaling functions. They provide a time frequency

analysis of the input signal by measuring the frequency content controlled by scale

factor, j at various times which is controlled by time shift n scaling coefficients c(n) &

wavelet coefficients d(j, n) are computed via inner products .Wavelets especially

Discrete Wavelet Transform based techniques are used in watermarking but the

results have not been up to the mark. The problems faced are that of oscillations ,shift

variance, lack of directionality etc. Wavelets especially discrete wavelet transform

have been employed in watermarking lately, As a result of extensive experimentation

we observe that watermarking with DTCWT gives much better results as compared to

discrete wavelet transform and because of addition of Arnold Transform the main

feature of security which is needed in watermarking also gets enhanced.DWT shows

limitations in case of complex signals such as radar, music, or geophysics &

imaging.CWT presents much more efficient representation for many types of

signals.More number of complex coefficients are obtained at various angles in case of

complex wavelet transform, which gives it an edge over DWT in case of

watermarking applications.[19] For this reason watermark can be embedded more

effectively with the help of complex wavelet transform. Wavelet coefficients are

widely spaced so it results in aliasing in DWT. Any wavelet coefficient processing

disturbs the delicate balance between forward and inverse transforms leading to

artifacts.


In reconstructed signal, DWT is lacking directional selectivity which greatly

complicates processing of edges etc. And causes severe limitations in case of

watermarking when it suffers attacks. If the watermarked image undergoes attacks

like rotation DWT would not be able to identify it as it cannot distinguish between

+45 &-45.[18]The basis of complex wavelets is the fourier transform ,as the

magnitude of fourier transform does not oscillate between positive and negative but

gives a smooth envelope in the fourier domain that is why we are able to retrieve

watermark effectively .Several experiments are carried out to measurethe performance

of the proposed watermarking in terms of imperceptibility and robustness against

attacks. A variety of images were tested for watermarking.Image quality is measured

using peak signal to noise ratio (PSNR) and mean square error (MSE). Lower the

value of MSE lower the error and better picture quality.

4.1 Peak Signal to Noise Ratio (PSNR)

For measuring image quality peak signal to noise ratio (PSNR) and mean square error

(MSE) are used. A low value of MSE indicates lower error and better picture quality.

Mean Square Error between original image and watermarked image is calculated as

follows:

𝑋 𝑖,𝑗 : Original image,

𝑋′ 𝑖,𝑗 : Watermarked image, and

𝑁𝑡 : Size of image

PSNR is calculated between the original and the watermarked image. The larger the

PSNR value, more similar is watermarked image to the original image. This image

quality metric is defined in decibels as:

The watermarked cameraman image with size 256×256 undergoing attacks are

shown in Fig. 3(a) These watermarked images are tested for various attacks namely

speckle noise, poisson noise,rotation ,salt and pepper noise and watermark is

extracted from the attacked images. The NC values are calculated for original

watermark and extracted watermark .Fig 3 below shows the proposed watermarking

rule applied to three images ,and the extracted watermarks.


Fig. 3. Watermarked images a Rice having PSNR=58.2384 and extracted watermark

in (d) with NC=1 (b) Cameraman having PSNR =58.2384 and extracted watermark in

(e) with NC=1 (c) Woman Blonde having PSNR=56.2135 dB and extracted

watermark in (f) with NC=1

Fig 4 below shows the proposed watermarking rule applied to three images and then

gaussian noise attack applied and the extracted watermarks in each case.


Fig. 4 Watermarked images with Gaussian noise Rice having PSNR=39.930 and

extracted watermark in (d) with NC=0.9998 (b) Cameraman having PSNR =40.0530

and extracted watermark in (e) with NC=0.9998 (c) Woman Blonde having

PSNR=39.9527 dB and extracted watermark in (f) with NC=0.9998.

Experiments were carried out to evaluate the performance of the proposed

watermarking method against different types of attacks like rotation, salt and pepper

noise, speckle noise ,Gaussian noise etc and the extracted watermark in each case.

(a)

Poisson Noise

(b)

Rotation 60

(c)

Salt

and pepper Noise

(d)

Gaussian Noise

0.01

PSNR =

40.1714

NC = 0.9998

PSNR

=29.7454

NC = 0.9981

PSNR =42.5652

NC = 0.9992

PSNR =

36.5953

NC = 0.9981

Fig 5 Cameraman image(256*256) under attacks i.e. Speckle, Poisson, Rotation,

Salt & Pepper & Gaussian noise & corresponding retrieved watermarks


While carrying out the experimentation , comparison is made as to how the results are

effected in case of using only a particular sub plane for retrieving the watermark and

using the reconstruction filter for the same. The values of Normalised correlation &

PSNR are compared for all the sub planes of DTCWT in order to see how close the

retrieved watermark is to the original watermark.

Performance under attacks

Experiments were carried out to evaluate the performance of the watermarking

method against different types of attacks like rotation, salt and pepper noise, speckle

noise ,Gaussian noise etc. Table 1 shows the comparison of watermark extraction

from different subplanes ,the PSNR & NC in each case and the embedding capacity

of the watermarking channel.A comparison chart of Normalised correlation wrt

different subplanes is shown in Fig 7.It is clear that watermark is effectively

recovered in each case.

Table 1. Comparison of PSNR values under attack of speckle noise (0.002)

Speckle Noise Image Cameraman

MSE RMSE PSNR(dB) NC Embedding

capacity

1 w1{j}{1}{1}{1} 0.0884 0.2973 42.7375 0.9997 5.4508

2 w1{j}{1}{2}{1} 0.3096 0.5565 42.7368 0.9998 5.4508

3 w1{j}{1}{2}{2} 0.0986 0.3410 42.7372 0.9997 5.4508

4 w1{j}{1}{2}{3} 0.0407 0.2018 42.7319 0.9997 5.4501

5 w1{j}{1}{1}{3} 0.0160 0.1266 42.7163 0.9997 5.4501

6 w1{j}{1}{1}{2} 0.0549 0.2344 42.7195 0.9997 5.4502

7 w1{j}{2}{1}{2} 0.0913 0.3022 42.7081 1 5.4498

8 w1{j}{2}{2}{2} 0.0922 0.3036 42.7375 1 5.4508

9 w1{j}{2}{2}{3} 0.2706 8.7683 42.7368 1 5.4508

10 w1{j}{2}{2}{1} 0.2707 0.5203 42.7372 1 5.4508

11 w1{j}{2}{1}{1} 0.2354 0.4852 42.7319 1 5.4506

12 w1{j}{2}{1}{3} 0.2351 0.489 42.7163 1 5.4501


Fig 7. Variation of NC in different subplanes of DTCWT.

It is observed that the value of PSNR is higher in case of w1{j}{2}{2}{2}&

w1{j}{1}{1}{1} and lowest in case of w1{j}{2}{1}{2} as is evident from the graph

but even then in all such cases it is seen the the value of Normalised correlation

varies within the range of 0.997 to 1 in all cases thus depicting that the retrieved

watermark is similar to the embedded watermark. Embedding capacity is better as

compared to the technique employed in [29].

Table 2. Comparison of PSNR ,MSE,RMSE & NC values under attack of poison

noise

Poisson noise Image Cameraman

MSE RMSE PSNR NC Embedding

Capacity

1 w1{j}{1}{1}{1} 39.8972 6.3164 40.1261 0.9977 5.3620

2 w1{j}{1}{2}{1} 39.4634 6.2820 40.1498 0.9974 5.3628

3 w1{j}{1}{2}{2} 39.2974 6.288 40.1590 0.9982 5.3631

4 w1{j}{1}{2}{3} 39.4986 6.2848 40.1479 0.9978 5.3627

5 w1{j}{1}{1}{3} 39.7964 6.3084 40.1316 0.9977 5.3622

6 w1{j}{1}{1}{2} 39.5199 6.2865 40.1467 0.9981 5.3627

7 w1{j}{2}{1}{2} 39.3027 6.2692 40.1587 0.9997 5.3631

8 w1{j}{2}{2}{2} 39.4156 6.2782 40.1525 0.9997 5.3629

9 w1{j}{2}{2}{3} 39.7160 6.3021 40.1360 0.9996 5.3623

10 w1{j}{2}{2}{1} 39.6134 6.2939 40.1416 0.9997 5.3625

11 w1{j}{2}{1}{1} 39.1881 6.2600 40.1650 0.9998 5.3633

12 w1{j}{2}{1}{3} 39.3654 6.2742 40.1552 0.9998 5.3630


A comparison chart of Normalised correlation wrt different subplanes is shown in Fig

8.It is clear that watermark is effectively recovered in each case.

Fig 8. Variation of PSNR in different subplanes of DTCWT.

Table 4 gives a comparison of proposed method in terms of rotation noise, speckle

noise, salt & pepper noise, gaussian noise and resizing with latest methods. In case of

rotation attack NC is 0.9988 and in other methods it is 0.7351[17'] &0.6841 in [14].In

case of speckle noise also NC is 0.9994 as compared to 0.9867 in [22]] .In case of salt

& pepper noise NC is 0.9963 as compared to 0.6573 in[17'].In case of gausian noise

NC is 0.9989 as compared to 0.9376 in[22] .Even in case of resizing attack NC is

equal to 1as compared to 0.9851 in [22].The performance of proposed method against

geometric attacks namely rotation and resizing is better in this case.

Table 3. Comparison of PSNR ,MSE,RMSE & NC values under mentioned attacks

and its comparison with latest methods

Image Proposed

Method

Ansari &

Pant[22]

Shishir

Kumar[26]

Bhatnagar &

Raman [28]

Bhatnagar&

Raman [27]

Rotation(60)* 0.9988 ---- 0.7351 0.6841 -----

Speckle Noise(0.01) 0.9994 0.9867 0.6743 0.6743 0.6644

Salt & Pepper

Noise(0.001)

0.9963 ---- 0.6573 0.6512 0.3557

Gaussian

Noise(0.01)

0.9989 0.9376 0.6684 0.6644 0.2575

Resizing

( 256->512->256)*

1 0.9851 0.8728 0.7619 ----

Note:* Indicates geometric attacks


The above experiments have been carried on a set of 9 images namely Rice(PSNR

51.8dB), Cameraman (PSNR = 51.8494 dB) , Woman Blonde (PSNR = 51.6284 dB) ,

Khairnar ( PSNR 51.6138 dB) , Pirate (PSNR 51.6101 dB) ,Mandrill (PSNR

51.6091 dB), Living Room( PSNR 51.6237 dB),Boat( PSNR = 51.6121 dB) ,Lena

(PSNR 51.6243 dB) .

CONCLUSION

We have presented an algorithm for increasing the security using Digital Image

Watermarking by Complex Wavelet Transform with Arnold Transform. And we have

basically compared the performances of different sub planes of DTCWT in terms of

RMSE, PSNR & NC. A detailed analysis is done for all the sub planes .It is found that

the value of PSNR obtained in all the sub planes is approximately 50dB,and the value

of NC is nearly equal to 1 which indicates that the retrieved watermark is nearly same

as embedded watermark. So with improved PSNR we have also added feature of

security. The algorithm shows robustness against some common signal attacks like,

salt and pepper noise, speckle noise, Gaussian noise, rotation etc. The watermark

survives even when the image undergoes attacks.

ACKNOWLEDGEMENTS

We would like to acknowledge the support offered by IK Gujral Punjab Technical

University, Jalandhar, Punjab ,India by means of providing access to the latest

journals and making available the resources.

REFERENCES

[1] N. G Kingsbury, “Image. processing with complex wavelet,” Phil. Trans. Roy.

Soc. London A, vol. 357, pp. 2543–2560, Sep. 1999.

[2] Radu O. Preda “Semi-fragile watermarking for image authentication with

sensitive tamper localization in the wavelet domain “University Politehnica of

Bucharest, Faculty of Electronics, Telecommunications and Information

Technology, Bucharest, Romania 21 July 2012.

[3] Manesh Kokare, P.K. Biswas, B.N. Chatterji, “Texture image retrieval using

new rotated complex wavelet filters”, IEEE transaction on systems, man, and

cybernetics-part b: cybernetics, Vol.35, no.6, December 2005. Y.

[4]. N. G Kingsbury, Natural Scenes, Vision & Wavelets CSP Forum, Signal

Processing and Communications Laboratory Department of Engineering,

University of Cambridge, UK.

[5] C.-T. Li, H. Si, Wavelet-based fragile watermarking scheme for image

authentication, Journal of Electronic Imaging 16 (1) (2007) 1–9.

[6] S.-H. Liu, H.-X. Yao, W. Gao, Y.-L. Liu, An image fragile watermark scheme


based on chaotic image pattern and pixel-pairs, Applied Mathematics and

Computation 185 (2) (2007) 869–882.

[7] A.H. Ouda, M.R. El-Sakka, Localization and security enhancement of block

based image authentication, in: Proc. of IEEE International Conference on

Image Processing (ICIP 2005), vol. 1, September 2005,

[8] H. He, J. Zhang, F. Chen, Adjacent-block based statistical detection method

for self-embedding watermarking techniques, Signal Processing 89 (2009)

1557–1566.

[9] H. Sadreazamia, M. Aminib,” A robust spread spectrum based image

watermarking in ridgelet domain” ,Int. J. Electron. Commun. (AEÜ) 66 (2012)

364– 371 Science Direct.

[10] Min Li1 Ting Liang,Yu-jie He,Min Li Xi’ , Arnold Transform Based Image

Scrambling Method

[11] A. Bouridane', F. KurugoIIu', R. Beggs'and S. Boussakta' ,”Colour Image

Watermarking In The Complex Wavelet Domain” ,School of Computer

Science, Queen's University,School of Electrical and Electronic Engineering,

The University of Leeds

[12] Patrick Loo , Nick Kingsbury,” Digital Watermarking Using Complex

Wavelets” Signal Processing Laboratory, Department of Engineering,

University of Cambridge.

[13] Digital Watermarking-Based DCT and JPEG Model Mohamed A. Suhail,

Member, IEEE, and Mohammad S. Obaidat, Senior Member, IEEE

Transactions On Instrumentation And Measurement, VOL. 52, NO. 5,

OCTOBER 2003.

[14] Complex-valued Wavelets, the Dual Tree,and Hilbert Pairs, Nick Kingsbury

Signal Processing and communications Laboratory Department of

Engineering, University of Cambridge, UK. Part 3, UDRC Short

Course,University of CAMBRIDGE,

[15] Arnold Transform Based Steganography International Journal of Soft

Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-3, Issue-4,

September 2013.

[16] Dual-Tree Complex Wavelets - their key properties and a range of image-

processing applications, Nick Kingsbury Signal Processing and

Communications Laboratory Department of Engineering, University of

Cambridge, UK.Part 1, UDRC Short Course, 21 March 2013

[17] D. Bhattacharyya, J. Dutta, P. Das, et al., Discrete cosine transformation based

image authentication and secret message transmission scheme, in: First

International Conference on Computational Intelligence, Communication

Systems and Networks, CICSYN ‘09, 2009, pp. 374–379.


[18] Complex Wavelets for Shift Invariant Analysis and Filtering of Signals Nick

Kingsbury Department of Engineering, Signal Processing Group, University

of Cambridge, Cambridge CB2 1PZ, United Kingdom.

[19] Watermarking Using Complex Wavelets With Resistance To Geometric

Distortion Patrick Loo Nick Kingsbury Department of Engineering, University

of Cambridge, United Kingdom

[20] Geometric correction based color image watermarking using fuzzy least

squares support vector machine and Bessel K form distribution Chunpeng

Wang, Xingyuan Wang⁎, Chuan Zhang, Zhiqiu Xia Faculty of Electronic

Information and Electrical Engineering, Dalian University of Technology,

Dalian 116023, China.

[21] Fragile Image Watermarking With Pixel-wise Recovery Based on

Overlapping Embedding Strategy Chuan Qin , Ping Ji , Xinpeng Zhang ,

Jing Dong , Jinwei Wang Signal Processing

(2017),doi:10.1016/j.sigpro.2017.03.033.

[22] Multipurpose image watermarking in the domain of DWT based on SVD and

ABC Irshad Ahmad Ansari ∗ , Millie Pant Department of ASE, Indian

Institute of Technology Roorkee, Roorkee 247001, India.Pattern Recognition

Letters (2016), http://dx.doi.org/10.1016/j.patrec.2016.12.010.

[23] Geometrically invariant and high capacity image watermarking scheme using

accurate radial transform Chandan Singh 1, SukhjeetK.Ranade n Department

of Computer Science, Punjabi University,Patiala147002,India Optics & Laser

Technology 54(2013)176–184

[24] Improving digital image watermarking by means of optimal channel selection

Thien Huynh-The a , Oresti Banos b , Sungyoung Lee a , ∗ , Yongik Yoon c ,

Thuong Le-Tien d Expert Systems With Applications

[25] A quality guaranteed robust image watermarking optimization with Artificial

Bee Colony Assem Mahmoud Abdelhakim a , ∗ , Hassan Ibrahim Saleh a ,

Amin Mohamed Nassar B A, Department of Radiation Engineering, Egyptian

Atomic Energy Authority, Cairo, Egypt b Department of Electronics and

Communication, Cairo University, Giza, Egypt

[26] Rough set based effective technique of image watermarkingShishir Kumara,∗ ,

Neha Jaina, Steven Lawrence FernandesbaDepartment of Computer Science &

Engineering, Jaypee University of Engineering & Guna, MP, IndiabDept of

Electronics & Communication Engineering Sahyadri College of Engineering

& Management, Mangalore, Karnataka, India Conference on Multimedia

Technology（ICMT 2013).

[27] Gaurav Bhatnagar, Balasubramanian Raman, A new robust reference

logowatermarking scheme, Multimedia Tools Appl. 52 (2–3) (2009) 621–640.

http://dx.doi.org/10.1016/j.patrec.2016.12.010


[28] Gaurav Bhatnagar, Balasubramanian Raman, A new robust

referencewatermarking scheme based on DWT-SVD, Comput. Stand.

Interfaces 31(September (5)) (2009) 1002–1013.

[29] https://books.google.co.in/books?id=pTojDgAAQBAJ&pg=PA584&lpg

=PA584&dq=C%

AUTHORS DETAILS:

Sukhwinder Kaur is working as an Assistant Professor in JSPM’s

Imperial College of Engineering & Research ,Pune since 2008 and is

presently pursuing PhD from IKGPTU Jalandhar. She is having an

experience of more than 14 years in Teaching . She received

M.Tech. in Electronics and Communication Engineering from Guru

Nanak Dev Engineering College in 2008 and B.Tech. Degree in

Electronics Engineering from Giani Zail Singh College of Engineering & Technology

in 2002. Her research interests are Digital Image watermarking, Image Denoising, She

has more than 10 research publications in various national and international journals

and conferences to his credit and has guided 12 M.Tech. students.

Dr. Rajneesh Talwar is presently working as Professor and

Principal, CGC- College of Engineering at Chandigarh group of

colleges,Jhanjeri, Punjab India. He has a Professional experience of

more than 15 years in teaching and research. He has a U.S patent

“FIBER OPTIC POINT TEMPERATURE SENSOR” to his credit,

six international Journal Publications, presented six papers at

International conferences and many national level conference participations. He is

Editorial board member of International Journal of Engg. Science and Technology,

Nigeria, Reviewer of MAEJO International Journal of Engineering Science and

Technology, Thailand and “Materials and Design”, a ELSEVIER International

Journal. `Already guided one M.Tech thesis and two more students are pursuing

M.Tech thesis under him. Presently guiding five candidates for their Phd.

work..Recently he has been conferred with Bharat Vidya Shiromani Award by

International Business Council.

https://books.google.co.in/books?id=pTojDgAAQBAJ&pg=PA584&lpg%20=PA584&dq=C%25

https://books.google.co.in/books?id=pTojDgAAQBAJ&pg=PA584&lpg%20=PA584&dq=C%25


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