development of iris template protection using lsbrn for...

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


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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


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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.


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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


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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


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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


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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.


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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.



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.


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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.

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