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IJCSNS International Journal of Computer Science and Network Security, VOL.19 No.7, July 2019
175
Manuscript received July 5, 2019
Manuscript revised July 20, 2019
Development of Iris Template Protection using LSBRN for
Biometrics Security
Z. Zainal Abidin1†, N. A. Zakaria2†and N. L. N. Mohd Sabri3††
INSFORNET, Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka
Summary In biometric security system, steganography is one of the
methods for securing biometric data template from unauthorized
user at the access control level in the computer network
infrastructure. However, due to increasing cases in hacking
activities, current steganography techniques for instance Least
Significant Bits (LSB) and Most Significant Bits (MSB), demand
an improvement for producing a better security mechanism.
Therefore, this study proposes a method from a combination of
LSB and random number, called as (LSBRN) to secure the
biometric template. The iris sensor captures the iris image and
extracts the iris codes. Then, the iris code is embedded using
LSBRN which converts secret messages to binary stream and
hides into a proper lower bit plane without destroying the
property of the cover image. In addition, LSBRN used a stego-
key and secret message while embedding messages inside the
cover image. By using the stego-key, the chance of getting
attacked by the attacker is reduced and the security of the
information in the data packet to be transmitted to destination
from detected by the attacker is higher. The experiment results
show parameters of peak of signal to noise ratio (PSNR) is
76.2512 dB and mean squared error (MSE) gives 0.0049. High in
PSNR value provides a better quality of image and low value of
MSE indicates a lower noise rate. As a conclusion, this study
brings a significant impact towards better security in
steganography and biometrics applications.
Key words: Iris Biometrics, Iris Template, Least Significant Bit, Random
Number.
1. Introduction
The human iris is a unique trait [1], which is poised of
pigmented vessels and ligaments forming unique linear
marks, slight ridges, grooves, furrows and vasculature [2].
The iris is a thin circular anatomical structure in the eye.
The iris’s function is to control the diameter and size of the
pupils and hence it controls the amount of light that
progresses to the retina. To control the amount of light
entering the eye, the muscles associated with the iris
(sphincter and dilator) either expand or contract the center
aperture of the iris known as the pupil. The iris is divided
into two basic regions: the pupillary zone, whose edges
form the boundary of the pupil and the ciliary zone which
constitutes the rest of the iris.
Fig. 1 Human iris.
Evaluating more features of the iris increases the chance of
uniqueness, since more features are being measured, it is
less possible for two irises to match [3]. In fact, iris
remains constant almost for a certain period of time [4] and
typically in 6 years [5] as it is not subjected to the
environment, as it is protected by the cornea and aqueous
humor. The pattern of one’s iris is fully formed by eleven
months of age and remains the similar till death. Iris
recognition is widely used for security purposes at access
control system since it provides high in accuracy,
authenticity and availability [6]. Moreover, iris technology
is accurate since it uses more than 241 points of reference
in iris pattern, as a basis for matching process [7].
Meanwhile, an iris template consists of iris code (in
binary) and iris template (in image) that has been created
or copied and stored in electronic form [8]. The iris code is
represented in 1’s and 0’s, but, an iris image can be
described in terms of vector graphics or raster graphics [9],
[10]. An image stored in raster form is sometimes called a
bitmap. An image map is a file containing information that
associates different locations on a specified image with
hypertext links. This numeric representation forms a grid
and the individual points are referred to as pixels (picture
element). Greyscale images use 8-bits for each pixel and
are able to display 256 different colours or shades of grey.
Digital colour images are typically stored in 24-bit pixels
and use the RGB colour model, also known as true colour.
All colour variations for the pixels of a 24-bit image are
derived from three primary colours: red, green and blue,
and each primary colour is represented by 8 bits. Thus, in
one given pixel, there can be 256 different quantities of red,
green and blue.
Biometrics work well only if the verifier can verify two
things, the biometric came from the genuine person at the
IJCSNS International Journal of Computer Science and Network Security, VOL.19 No.7, July 2019
176
time of verification and the biometric matches the
biometric template information in the database. However,
a variety of problems hinder the ability to verify the
genuine person such as:
i. Noise in acquired data – Noisy biometric data
caused by defective sensors, defective physical
characteristics and unfavorable ambient
conditions, which causes the data to be incorrectly
matched or incorrectly rejected [11].
ii. Intra-class variations – The data acquired during
authentication may be different from the data used
to generate the template during enrollment,
affecting the matching process [12].
iii. Distinctiveness – Every biometric trait has an
upper bound in terms of its discrimination
capabilities [13].
iv. Non-universality – A subset of the users not
possessing a particular biometric [14].
v. Overriding biometric template in database [15] –
security and privacy of the biometric template in
the database is exposed to intruders.
vi. Copyright Issue – copyright crime is a serious
issue and hacking activities seems interrupted
most of the main points in biometric system
security which, cause the biometric system
become insecure and vulnerability to those who
use it [16].
As moving towards the industrial revolution, biometric
data is gaining important attention as biometric
information is dependent on the raw facts. The exchange of
information is required to share resources among the
distributed users, which may be separated by locations [17].
While transferring information among users, the important
aspect to be considered is the confidentiality and privacy
should be maintained. To meet the privacy and
confidentiality requirements, technique of steganography is
used since different mediums to hide the data that are text,
images, audio and video from the cover image is crucial
[18].
Therefore, the digitally shared biometric information
between the users should be converted to some unreadable
format which cannot be tampered by the intruders. In fact,
steganography is concerned with concealing the fact that a
secret message is being sent, as well as concealing the
contents of the message [19]. On the other hand,
cryptography is the practice of protecting the contents of a
message alone. Moreover, cryptography protects the
contents of a message, meanwhile, steganography is to
protect contents of a message and communicating channel.
There are cases when the iris image can be fooled and
easily copied by the hacker.
Thus, the advantage of steganography over cryptography is
the intended secret message does not attract attention to
itself as an object of inspection [20]. In fact, the curiosity
of intruders to read the intended message along the
transmission channel (wired / wireless) is decreased, since
steganography provides a camouflage mechanism to the
biometric template and protect the information from
danger.
The objective of this research is to compare the existing
techniques in steganography such as LSB, MSB, wavelets
and LSBRN used to hide information of the iris biometric
template. Also, to find the quality of iris images
performance after compression (based on PSNR and MSE
rates) in iris recognition.
The contribution of this study is to enhanced an existing
steganography method, which is LSB and evaluate iris
template with other techniques based on PSNR and MSE.
2. Related Works
2.1 Iris Recognition
Biometric utilize physical traits (gait and voice
recognition) or behavioral characteristics (iris, retina,
thumbprint and face) for a reliable identity of
authentication. The usage of iris biometric technology and
application has increased tremendously for its user
friendliness, performance, permanence, accuracy and
uniqueness [21]. There are many systems and machines use
biometric in daily activities for instance, attendance system,
withdrawing money from ATM and thumbprint to switch
on laptop. In fact, in biometric, human is the key to access
systems. Biometrics template is useful to the access control
system [22]. The biometric data is easy to steal or leading
to identity theft and not secured [23]. The more a biometric
data is used, the less secret it would be.
2.2 Least Significant Bit (LSB)
Sending message of biometric templates in frequent in
transmission channels draw an attention of third parties, i.e.
crackers and hackers, which, create attempts in revealing
the original message. Thus, the art of hiding information of
a message inside a cover media is highly demanded in
biometrics since it reduce the eavesdropper’s intention to
hacking.
The objective is to propose a combination of the existing
least significant bit (LSB) approach with random numbers
(RN) for better security performance. The reasons why
LSB is mostly used in spatial domain steganography [24]
and makes the perceptual message invisible but has the
computational complexity [25].
Capacity, security and robustness are the three main
aspects affecting steganography and its usefulness [26].
Capacity refers to the amount of data bits that can be
IJCSNS International Journal of Computer Science and Network Security, VOL.19 No.7, July 2019
177
hidden in the cover medium. Security relates to the ability
of an eavesdropper to point the hidden information easily.
Robustness is concerned with the resist ant probability of
modifying or destroying the unseen data. The main
function is to transmit a message through some innocuous
carrier, for example, text, image, audio and video over a
communication channel.
Therefore, the proposed approach is directly hiding secret
data into each pixel in an image. Then, based on the LSB
technique, an algorithm for 24-bit colour image is
developed to improve the stego-image quality of colour
image. In LSB steganography, the least significant bits of
the cover media’s digital data are used to conceal the
secret message. LSB Steganography can be classified by
two methods LSB replacement and LSB matching [27].
LSB replacement steganography replace the last bits of
cover image with each bits of the message that needs to be
hidden. Algorithm for LSB replacement consists of
embedding and extracting process that is given as:
A LSB-based Embedding Algorithm
Input -: cover C
for i = 1 to Length(c), do
Sj ← Cj
for i = 1 to Length (m), do Compute index ji where to store the ith message bit of m
Sji ← LSB (Cji) = mi
End for
Output -: Stego image S
A LSB-based Extracting Algorithm
Input -: Secret image s
for i = 1 to Length (m), do Compute index ji where to store the ith message bit of m
mji ← LSB (Cji)
End for
In the extraction process, the embedded messages can be
readily extracted without referring to the original cover-
image from the given stego-image S. The set of pixels
storing the secret message bits are selected from the stego-
image, using the same sequence as in the embedding
process. The n LSBs of the selected pixels are extracted
and lined up to reconstruct the secret message bits.
On the other hand, in LSB matching, if the bit must change,
the operation of ±1 is applied to the pixel value. The use of
+ or - is chosen randomly and has no effect on the hidden
message. The detectors for both LSB replacement and ±1
embedding work the same way: the LSB for each selected
pixel is the hidden bit. LSB technique is easy to implement
and has a potentially large payload capacity [17].
Furthermore, LSB matching detects the existence of secret
messages embedded by LSB embedding in digital image.
The iris biometric template security using steganography is
shown as in Figure 2.
Fig. 2 Iris Biometric Template Security using Steganography [17]
3. Methods
An information hiding has been developed for
confidentiality. The carrier is known as cover-image, while
stego-object known as stego-image. The security of the
biometric template is the most important factor for
securing the biometric system. The most dangerous attack
on biometric system is against the template database. To
overcome the problem related to biometric template
security, an approach is presented for securing iris
templates as illustrated in Figure 3.
IJCSNS International Journal of Computer Science and Network Security, VOL.19 No.7, July 2019
178
Fig. 3 The LSBRN Approach in Iris Recognition System
In addition, biometric consists of two general processes
which is the enrolment and verification. The enrolment
process collects the biometric templates images, which is
the iris image using extraction algorithms. Meanwhile, the
verification stage involves matching and detraction
algorithms. The integration of the steganography
properties is implemented into the biometric enrolment and
verification process. Steganography has three properties
which are:
Key generation algorithm (K): takes as input
parameter n and outputs a bit string k, called the
stego key. Combine random noise with the stego
key.
Steganographic encoding algorithm (E): takes as
inputs the security parameter n, the stego key (k)
and a message (m), {0, 1} l, to be embedded and
outputs an element c of the cover image space C,
which is called iris stego. The algorithm may
access the cover image distribution C.
Steganographic decoding algorithm (D): takes as
inputs the security parameter n, the stego key (k),
and an element c of the cover image space C and
outputs either a message m {0, 1} l or a special
symbol (?). An output value of indicates a
decoding error, for example, when SD has
determined that no message is embedded in c.
A stego-image is obtained by applying LSB algorithm on
both the cover and hidden image. The hidden image is
extracted from the stego-image by applying the reverse
IJCSNS International Journal of Computer Science and Network Security, VOL.19 No.7, July 2019
179
process. If the LSB of the pixel value of cover image is
C(I,j) is equal to the message bit m of secret message to be
embedding procedure is given below:
S(i,j) = C(i,j) - 1, if
LSB(C(i,j)) = 1 and m = 0
S(i.j) = C(i,j), if LSB(C(i,j)) = m
S(i,j) = C(i,j) + 1,
if LSB(C(i,j)) = 0 and m = 1
where LSB (C (i, j)) stands for the LSB of cover image
C(i,j) and m is the next message bit t where LSB(C(i, j))
stands for the LSB of cover image C(i,j) and m is the next
message bit to be embedded. S(i,j) is the stego-image. As
we already know each pixel is made up of three bytes
consisting of either a 1 or a 0. For example, suppose one
can hide a message in three pixels of an image (24-bit
colours). Suppose the original 3 pixels are:
i. (11101010 11101000 11001011)
ii. (01100110 11001010 11101000)
iii. (11001001 00100101 11101001)
A steganography program could hide the letter "J" which
has a position 74 into ASCII character set and have a
binary representation "01001010", by altering the channel
bits of pixels.
i. (11101010 11101001 11001010)
ii. (01100110 11001011 11101000)
iii. (11001001 00100100 11101001)
In this case, only four bits needed to be changed to insert
the character successfully. The resulting changes that are
made to the least significant bits are too small to be
recognized by the human eye, so the message is actively
hidden. The advantage of LSB embedding is its simplicity
and many techniques use these methods. LSB embedding
also allows high perceptual transparency.
3.1 Data Embedding
The embedding process involves three elements:
i. Read cover image
ii. Input hidden messages
iii. Output stego-image
Step 1: Read cover image.
Step 2: Extract the pixels of the cover image.
Step 3: Extract the character of the text file.
Step 4: Choose first pixel and place it in first component of
pixel.
Step 5: Place some terminating symbol to indicate end of
the key.
Step 6: Insert characters of text file in each last component
of the next pixels by replacing it.
Step 7: Repeat step 6 till all the characters has been
embedded.
Step 8: Obtain stego-image
3.2 Data Extraction
The extracting process involves two entities that are:
i. Input stego-image, stego key
ii. Output secret text message
Step 1: Extract the pixel of the stego-image.
Step 2: Start from first pixel and extract stego key
characters from first component of the pixels. Place some
terminating symbol to indicate end of the key.
Step 4: If this extracted key matches with the key entered
by the receiver, then follow step 5, otherwise terminate the
program.
Step 5: If the key is correct, then go to next pixels and
extract secret message characters’ form first component of
the next pixels. Follow step 5 till up to terminating symbol,
otherwise follow step 6.
Step 6: Extract secret message.
3.3 Image Encoding Algorithm
Encoding Process
i. Input image file,
ii. Read Stego key and image file
iii. Output stego-image
iv.
1) The cover and secret image are read and
converted into the unit8 type.
2) The number in secret image matrix are conveyed
to 8-bit binary. Then the matrix is reshaped to a
new matrix a.
3) The matrix of the cover image is also reshaped to
matrix b.
4) Perform the LSB technique described above.
5) The stego-image, which is similar to the original
cover image, is achieved by reshaping matrix b.
6) While extracting the data, the LSB of the stego-
image is collected and they are reconstructed into
decimal numbers. The decimal numbers are
reshaped to the secret image.
3.4 Extraction of Hidden Message
In this process of extraction, the process first takes the key
and then random-key. These keys take out the points of the
LSB where the secret message is randomly distributed.
Decoding process searches the hidden bits of a secret
message into the least significant bit of the pixels within a
cover image using the random key.
In decoding algorithm, the random-key must match i.e. the
random-key which was used in encoding should match
IJCSNS International Journal of Computer Science and Network Security, VOL.19 No.7, July 2019
180
because the random key sets the hiding points of the
message in case of encoding. Then receiver can extract the
embedded messages exactly using only the stego-key.
3.4.1 Message extraction algorithm
Inputs stego-image file, stego-key, random key.
Output secret text message.
Step 1: Open the stego image file and read the RGB colour
of each pixel.
Step 2: Extract the red component of the host image.
Step 3: Read the last bit of each pixel.
Step 4: Initialize the random-key that gives the positons of
the message bits in the red pixels that are embedded
randomly.
Step 5: For decoding, select the pixels and extract the LSB
value of red pixels.
Step 6: Read each of the pixels then content of the array
converts into decimal value that is ASCII value hidden
character.
Step 7: ASCII values got from above is XOR with stego-
key and gives message file, which hidden inside the cover
image.
The most crucial element for securing biometric system is
biometric template security. Therefore, the most dangerous
attack on an iris recognition system as shown in Figure 4 in
biometrics is against the template database. One of many
security techniques has been presenting to sort out the
problems related with database template security.
The technique use is LSB approach steganography for
securing iris authentication. There is some possibility
where an attacker succeeds in gaining unauthorized access
to processed templates even though the steganography had
been applied. However, it would be almost impossible for
attacker to access the original iris data embedded in cover
image.
4. Results and Discussions
Biometric recognition system which rely on physical and
behavioural features of the human body to recognize a
human-being, are used in various area that require a high
degree of security. A steganographic algorithm for 8-bit
(grayscale) or 24-bit (colour image) is presented based on
Logical operation. Algorithm embedded ASCII code of
text in to LSB of cover image involves elements such as:
i. Cover-Image: An image in which the secret
information is going to be hidden. The term
"cover" is used to describe the original, innocent
message, data, audio, still, video etc. The cover
image is sometimes called as the "host".
ii. Stego-Image: The medium in which the
information is hidden. The "stego" data is the data
containing both the cover image and the
"embedded" information. Logically, the
processing of hiding the secret information in the
cover image is known as embedding.
iii. Payload: The information which is to be
concealed. The information to be hidden in the
cover data is known as the "embedded" data.
iv. Secret key: This is the key used as a password to
encrypt and decrypt the cover and stego
respectively in order to extract the hidden
message. Secret key is optional.
Fig. 4 Hiding data in iris template (Iris Code)
Based on Figure 4, the iris stego-image and iris cover
image are obtained through enrolment process using
LSBRN approach. One can retrieve back the secret
message in order to gain an originality of Iris Code in
biometric system as shown in Figure 5.
Fig. 5 Secret message embedded into cover image
4.1 Data Embedding
In steganography technique PSNR (Peak Signal-to-Noise
Ratio), MSE (Mean Squared Error) and SNR (Signal-to-
Noise Ratio) are standard measurement used in order to
test the quality of the stego-image. MSE measures the
IJCSNS International Journal of Computer Science and Network Security, VOL.19 No.7, July 2019
181
average of the squares of the errors. The error is the
amount by which the pixel value implied by the stego-
image differs from the cover image. PSNR, define ratio
between the maximum possible power of a signal and the
power of corrupting noise that affects the fidelity of its
representation. The signal in this case is the cover image,
and the noise is the error introduced by bits of secret image.
Higher the value of PSNR, more the quality of the stego-
image. Let us consider, the cover image C of size M × M
and the stego-image is S of size N × N, then each cover
image C and stego-image S has pixel value (x, y) from 0 to
M-1 and 0 to N-1 respectively. The PSNR and MSE is
then calculated as follows:
Fig. 6 Formula of MSE and PSNR
Here, αi,j is the pixel of the cover image where the
coordinate is (i, j), and βi,j is the pixel of the stego-image
where the coordinate is (i, j). M and N represent the size of
the image. A larger PSNR value indicates that the
difference between cover image and the stego-image is
more invisible to the human eye.
Table 1: Comparison of SNR, MSE and PSNR Noise Rate 8 bits image (LSB data hiding)
SNR 66.0325 MSE 0.0059 PSNR 70.4330
Thus, the obtained experiment result show that the higher
noise rate between cover image and stego-image seems
more invisible and almost identical compare to cover
image through human eye.
4.2 Data Extraction
In proposed approach, LSBRN technique is used to hide
Iris Code in cover image. The iris code is stored as iris
stego-image resulting after hiding Iris Code in cover image,
which has the following steps.
Step 1: Capture Iris image from sensor.
Step 2: Extract Iris feature set (Iris code) from Iris image.
Step 3: Apply LSB Embedding Process
a) Select a 24-bit cover image.
b) Get Iris code to be hidden in cover image.
c) Get least significant bits of blue colour
component only to hide Iris code.
d) Generate random sequence of bits with random ( )
function of MATLAB to obtain least significant
bit positions to hide Iris code.
e) Hide the Iris code in cover image.
f) Resulting image is the iris stego-image.
g) Store iris stego-image as template in Iris database.
To hide iris code in cover image, least significant bits of
only blue colour component from all RGB values of a
pixel are used. This is because the distortion of pixels is
less if only one colour component out of RGB is used. To
embed Iris code, random sequence of least significant bit
from random colour component is replaced. Suppose a
sequence of bits ‘011001110100’ is taken from Iris code.
Let the random sequence generated by random number
generator function is 766877678688. According to this
random sequence, least significant bit positions to be
replaced are represented by grey cells of Table 2. Table 3
shows the changes made after applying least significant bit
steganography, which it is clear that only five least
significant bits are replaced with Iris code bits. Rest of the
values is already present in the cover image. So, there is no
need to replace them. The same process is repeated for the
rest of Iris code bits. The resulting stego-image is stored as
Iris Stego-image in the database.
Table 2: Actual LSB of Random Colour Component Bits
Table 3: Actual LSB of Random Colour Component Bits
4.3 Data Encoding and Decoding
The steps for LSB explain the procedure of data hiding and
encoding the text in an iris image.
Step1: A few least significant bits (LSB) are substituted
with in data to be hidden.
IJCSNS International Journal of Computer Science and Network Security, VOL.19 No.7, July 2019
182
Step2: The pixels are arranged in a manner of placing the
hidden bits before the pixel of each cover image to
minimize the errors.
Step3: Let n LSBs be substituted in each pixel.
Step4: Let d = decimal value of the pixel after the
substitution. d1 = decimal value of last n bits of the pixel.
d2 = decimal value of n bits hidden in that pixel.
Step5: If (d1~d2) <= (2^n)/2 then no adjustment is made in
that pixel. Else
Step6: If (d1<d2) d = d – 2^n. If (d1>d2)d = d + 2^n.This
‘ d ’ is converted to binary and written back to pixel. This
method of substitution is simple and easy to retrieve the
data and the image quality is better and provides good
security.
The encoder algorithm is as given below:
1: for i = 1, len (msg) do
2: p = LSB (pixel of the image)
3: if p!= message bit then
4: pixel of the image = message bit
5: end if
6: end for
Data decoding is achieved by calculating the modulus 2 of
the pixel value and return a “0” if then number is even, and
a “1” if the number is odd. The value is compared with the
message bits of iris stego. If they are the same, then do
nothing, but if they are different, then the pixel value with
the message bit need to be replaced. This process
continues even though there are still values in the message
that need to be encoded. The decoder algorithm is:
1: for i = 1, len (image string)
do2: message string = LSB (pixel string of the image) 3
end for
The decoding phase is even simpler. As the encoder
replaced the LSBs of the pixel values in c in sequence, it is
noticeable from the order used to retrieve the data.
Therefore, calculate the modulus 2 of all the pixel values,
and construct m as m0.
To evaluate the data embedding, data hiding and encoding,
find the selected file in MATLAB “cd ‘file_directory’ and
display the document “ls” as shown in Figure 7.
Fig. 7 MATLAB commands to find image with data hiding
Then, double click on script file ‘LSBNoiseLink’. Right
click and choose the “Evaluate Selection” as in Figure 8.
Fig. 8 Evaluate Selection Option
After select the “Evaluate Selection”, an interface shows
the results, which are the stego image, cover image (in
Figure 9), recovering information (in Figure 10), SNR,
MSE and PSNR values (in Figure 11).
Fig. 9 Iris Recognition System.
Fig. 10 Iris Recognition System.
Fig. 11 Iris Recognition System.
The results indicate that experiments conducted produce
output of PSNR, MSE and SNR, which each gives 75.2705
dB, 0.0019 and 70.87 in MATLAB. Moreover, the
performance of PSNR and MSE is evaluated with other
techniques such as LSB, MSB, DWT and DCT for a
comparison as illustrated in Table 4.
IJCSNS International Journal of Computer Science and Network Security, VOL.19 No.7, July 2019
183
Table 4: Comparison of PSNR and MSE values
Method PSNR Value
MSE Value
Most Significant Bit (MSB) 69.8585 0.0067 Discrete Cosine Transform (DCT) 70.5440 0.0057
Discrete Wavelength Transform (DWT) 75.5463 0.0018 Least Significant Bit (LSB) 75.3378 0.0019
Least Significant Bit with Random Number (LSB + RN)
76.2512 0.0049
The graph shows the differences between LSBRN with
other methods based on PSNR value.
Table 5: Comparison of PSNR value in graph of LSBRN and other
methods PSNR PSNR
LSBRN
LSB
LSBRN
MSB
LSBRN
DCT
LSBRN
DWT
5. Conclusions
The study explores on existing steganography approach
protecting biometric template especially in iris recognition
in system. The proposed method based on Steganography
has been performed to protect the iris template. Iris code is
hidden across random least significant bits of cover image.
Distortion produced by steganography is negligible as the
random colour component from RGB is used to hold the
iris code bits. However, without knowing the random
sequence of bits it is impossible for an imposter to find out
which LSB holds the iris code bits. Experiments are
carried out to examine the performance of the proposed
approach. The MSE and PSNR values show that the image
quality is useful for data hiding, data confidentiality and
privacy in transmission channel. In addition, the
comparison of previous implementations and future model
promise a successful achievement. Lastly, the contribution
of this study is to combined an existing steganography
approach and develop a data hiding for iris template to
protect the biometric template against unauthorized attack
and making the iris biometric system more secure with the
implementation of steganography.
Acknowledgments
Thank you to research group C-ACT - INSFORNET,
Fakulti Teknologi Maklumat dan Komunikasi and
Universiti Teknikal Malaysia Melaka.
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Zaheera Zainal Abidin received Bachelor
of Information Technology from University
of Canberra, Australia in 2002. She joined
ExxonMobil Kuala Lumpur Regional
Center as a Project Analyst in 2000-2001.
She completed her MSc. In Quantitative
Sciences (2004), MSc. in Computer
Networking (2008) and PhD in I.T. and
Quantitative Sciences (2016) from Faculty
of Computer and Mathematical Sciences, Universiti Teknologi
MARA, Shah Alam, Selangor. She served as a lecturer at
Universiti Kuala Lumpur (2005-2009) and senior lecturer &
researcher in Universiti Teknikal Malaysia Melaka (2009 –
present). She is a member of Information Security, Forensics and
Networking (INSFORNET) research group. She is one of the
certified CISCO Academy (CCNA) in computer networking field
and certified Internet-of-Things specialists. Research interest in
Internet-of-Things, biometrics, network security and image
processing.
Nurul Azma Zakaria received Bachelor of
Engineering (Electronic Computer
Systems) from University of Salford,
United Kingdom (1999). She joined Maxis
Communications Berhad as a Software
Engineer. She completed her MSc. in
Information Systems Engineering (2002)
from University of Manchester Institute of
Science and Technology (UMIST), United
Kingdom and PhD in Information and
Mathematical Sciences (System-level Design) (2010) from
Saitama University. She is currently a senior lecturer at Faculty
of Information and Communication Technology, Universiti
Teknikal Malaysia Melaka (UTeM) and also a member of
Information Security, Forensics and Networking (INSFORNET)
research group. Her area of research interests includes computer
system and networking, embedded system design, IoT devices
and application, and IPv6 Migration.
Nur Liyana Nadhirah Mohd Sabri
received Bachelor of Computer Science
(Computer Security) from Universiti
Teknikal Malaysia Melaka (UTeM) in
2018. Currently, she is doing her Master of
Science (Security of Science Computer) in
UTeM. Her research interest is in
information security.