A Steganographic Technique for Highly Compressed JPEG Images

Shamsul Kamal Ahmad Khalid1, Mustafa Mat Deris

1 and

Kamaruddin Malik Mohamad1

1Faculty of Information Technology and Multimedia

Universiti Tun Hussein Onn Malaysia

Parit Raja, Batu Pahat 86400, Johor, Malaysia

shamsulk@uthm.edu.my, mmustafa@uthm.edu.my

malik@uthm.edu.my

ABSTRACT

Hiding secret data in JPEG images has been popularly

studied in steganography research. Most of the

previous studies propose schemes that hide secret data

in the quantized DCT coefficients. However, such

schemes either reduce the visual imperceptibility of the

compressed images or change its pixel histogram

properties, amounting to detection by statistical

attacks. This gets worst with highly compressed JPEG

images. In this paper, a new scheme for highly

compressed images is proposed. Using an area known

as NFDV, secret messages can be embedded with

minimal effect to changes in its pixel values. The

scheme also integrates a novel message randomizer

and message integrity checking sequence (MIX) to

protect the confidentiality and integrity of the secret

messages. Several test cases have been implemented,

and it shows that the proposed technique produces

highly imperceptible stego-images that are also secure

against pixel histogram statistical attacks.

KEYWORDS

Steganography, Information Hiding, High

Imperceptibility, DQT, JPEG.

1 INTRODUCTION

While a good cryptographic algorithm scrambles a

message to an unreadable form [1], a

steganographic algorithm hides a message inside

an unsuspecting cover media [2]. A few

requirements must be satisfied to protect the

message from being detected. A secure hiding

scheme should not produce perceptible change in

the output image. It should not be detectable by

any adversary analyzing the image by a naked eye

or by any statistical mean. Thus, two aspects are

usually addressed. Firstly, the embedding process

should not degrade the media’s fidelity [3].

Secondly, the embedding process should not

change the cover media’s statistical properties to

the point detectable by a statistical attack [4]. In

many steganography and watermarking systems,

PSNR (Peak Signal to Noise Ratio) and several

well-known statistical analysis attacks were used

to measure the imperceptibility and security of the

hiding scheme.

Hiding secret data in JPEG images has been

popularly studied in steganography research.

Common approaches used to hide messages in

JPEG images include least significant bit insertion

(LSB), masking and filtering, algorithms and

transformations. However, LSB is still the most

popular choice due to its simple embedding

concept and ease of implementation. Most

popular JPEG steganography tools like Jsteg,

Outguess, F5, JPHide and Steghide are using LSB

insertion technique. There are few drawbacks with

these tools. Firstly, their methods distort the

fidelity of the cover image by choosing the

quantized DCT coefficients as their concealment

locations. Similar to their predecessors, more

recent techniques have also marginally improved

the PSNR scores [5]. Secondly, their methods

distort the statistical properties of the cover image

by changing significantly the stego image’s pixel

histogram information. Both distortions

contribute negatively towards detection and

attacks. Thirdly, most of these techniques lack

integrity checker which make them vulnerable to

message modification. Although Steghide used

standard CRC-32, it is quite expensive for small

images, and with fixed message length, it may

ISBN: 978-0-9891305-2-3 ©2013 SDIWC 107

allow attackers to manually recalculate the CRC

value, thereby rendering the integrity checking

scheme ineffective.

In this paper, a new hiding scheme for highly

compressed JPEG images is proposed. Using an

area known as non functional high frequency DQT

values (NFDV), secret messages can be embedded

with minimal effect to pixel values’ changes. The

scheme also integrates a novel message

randomizer and message integrity checking

sequence to protect the confidentiality and

integrity of the secret messages. The secret

messages and the sequence are integrated,

encrypted and well blended into the DQT values.

The stego-images obtained from the proposed

system, produces far better imperceptibility score

than most previous techniques employing

modified DQT.

The rest of the paper is organized as follows.

Section 2 describes related work. Section 3

describes the proposed technique. Section 4

describes the result and discussion. Finally, the

conclusion of this work is described in section 5.

2 RELATED WORK

In this section, stego-systems based on default

DQT is reviewed and followed by stego-systems

based on modified DQT.

2.1 Default DQT stego-systems

There are many steganographic techniques and

tools that operate using default DQT. For

example, Derek Upham’s JSteg was the first

publicly available steganographic system for

JPEG images [6] using default DQT to embed

messages in the quantized DCT coefficients. The

message, which is limited to 40 bits and is

encrypted using RC4 stream cipher, is sequentially

embedded in the LSB of the DCT coefficients.

Due to this, anyone who knows the steganographic

system can determine the length and retrieve the

encrypted message hidden by Jsteg. Brute force

method can be applied to decrypt the recovered

encrypted message. Using similar technique, Niels

Provos initially introduced a hiding scheme called

F5 [7] and later on developed another alternative

method called Outguess [8]. F5 embeds secret bits

in the DCT coefficients using matrix embedding

so that for a given message the number of changes

made to the cover image is minimized. Outguess

version 0.3b embeds information in the LSB of the

DCT coefficients by making a random walk,

leaving some coefficients unchanged. This

algorithm improves the hiding by using a pseudo-

random number generator to select DCT

coefficients at random. However, the schemes

significantly change the pixel statistical property

of the image.

Like Jsteg, Outguess, F5, JPHide [9] and

Steghide, newer stego-systems also use default

DQT and embed secret messages in the

transformed DCT coefficients of the cover image.

In these systems, the LSB embedding was

designed to randomly change the coefficients as

minimum as possible, for example, in [10] and

[11]. However, minimizing changes from its

original coefficients is still a challenge because

more advanced statistical techniques are

continuously being developed to detect small

changes in the pixel histogram property of the

produced stego-images. Alternatively, a branch of

researchers exploit modified DQT to produce

better quality stego images.

2.2 Modified DQT stego-systems

Quantization table is not part of the JPEG

standard. Anyone is allowed to design and

redefine the quantization table to control the

quality of the reconstructed image and the

compression ratio [12]. In other words, a DQT

table can be arbitrarily generated. Realizing this,

many different methods have been proposed based

on modified quantization table to get better quality

stego-images [13][14]. This is possible because

human visual system (HVS) is more sensitive

towards the lower frequency signals.

For example, Chang et al. [15] argue that

since the energy of images is concentrated in the

lower frequency coefficients, modifying such

coefficients may degrades the quality of the stego-

image. Conversely, higher frequency coefficients

will be discarded due to the lossy quantization

process. Therefore, they suggested a modified

DQT in which the middle frequencies of the DQT

are replaced by all ones. By utilizing the 2-LSBs

ISBN: 978-0-9891305-2-3 ©2013 SDIWC 108

of the middle frequency coefficients, for every 8

by 8 block, 2X26=52 bits of secret messages were

embedded. They reported a PSNR range of 28-38

dB for various standard images. Similarly, by

enlarging the middle frequency space and using

arbitrary 16 by 16 DQT, Adel Almohammad et al.

[5] improved the capacity and imperceptibility of

the stego images by using optimized DQT table

based on Monro and Sherlock model. However,

all of these techniques embed messages in the

LSB of the DCT coefficients which directly affect

the components information, which in turn reduce

the fidelity and change the statistical property of

the final stego images. Both luminance and

chrominance DCT values were changed in these

methods.

The most recent work on modified DQT was

conceptually proposed by us in [16]. Instead of

embedding the secret messages in the DCT

coefficients with modified DQT, we embed the

secret messages directly into one of the DQT

table. However, we arbitrarily choose a high

quality JPEG image to embed secret messages.

Due to this choice, the embedding scheme

produces visible artifacts in its stego images.

Noticeably, at 24 bytes of message insertion,

distortions of the image can be clearly seen. To

protect the secret messages, we used a scattering

algorithm to randomly spread the message, but it

is predictable. Anyone who read the published

algorithm will be able to locate and retrieve where

the message were embedded. Finally, it lacked an

integrity checker that can protect the message

from modification.

3 THE NON FUNCTIONAL DQT VALUE

TECHNIQUE (NFDV)

The NFDV technique employs three strategies to

embed secret message with the least effect to the

fidelity and statistical property of the stego

images. The first strategy is illustrated in Figure

1. Figure 1(a) demonstrates the normal process

done in a stego system based on DCT transform

and LSB insertion in the quantized DCT

coefficients of a cover image [17]. The left of the

vertical bars are the embedding process and on the

right is the retrieving process to get back the secret

messages. In the proposed model (Figure 1(b)),

the secret messages are embedded into the bottom

right corner of the 8 by 8 DQT table (i.e. in the

high frequency area), after the quantization

process has been completed. The red lines signify

the cutoff points where the high frequency

coefficients will be zero-out to achieve good

compression without sacrificing the visual quality

of the original image. This area is referred as non

functional DQT values, hence NFDV. It is

stipulated that if the secret message is embedded

in the NFDV area of the DQT table but not

exceeding the red line (i.e. going in the upper left

direction), it will not have any effect on the

reconstructed image. This is due to the DCT

coefficients in this area will be zero out anyway.

Therefore, as no quantized DCT coefficients are

changed at all (i.e. embedded with a secret

message), the reconstructed image will be similar

to its cover image, without any change to its pixel

histogram information. Both luminance and

chrominance DQT tables can be used for

embedding secret messages.

Compressing the image with higher ratio will

move the red line nearer to the upper left corner of

the 8 by 8 box to zero out more high frequency

coefficients. Therefore, the second strategy is to

use a cover image that has been pre-compressed

with 20%-30% compression ratios. By doing that,

more secret messages can be embedded into the

DQT table and yet the outcome will be still similar

to the compressed cover image. 60% of

photographic images communicated on Internet

are compressed at 10-50% compression ratio [18].

The effect of the compression ratio is

illustrated in Table 1. Notice that 50% JPEG

compression is not giving any noticeable

distortions from the original images, i.e the quality

of the image is still good. The method in [16]

writes messages in the chrominance DQT far

beyond the red line. As a result, the stego image

quality and its statistical properties get seriously

affected. Using our method, the red line is moved

to the upper left corner of the 8 by 8 DQT table,

creating more non functional DQT values for the

purpose of embedding the secret messages.

ISBN: 978-0-9891305-2-3 ©2013 SDIWC 109

DCT Coefficients DQT Quantized DCT

Coefficients

Secret

Messages

/ =

(a) Normal JPEG Stego Model

Stego

ImageDQT

* =

DCT CoefficientsRaw

BMP/

TIFF Quantization Process Dequantization Process

0 0

0 00

0

0

0

0

0

Quantized DCT

Coefficients

00

Stego

Image

Embed Retrieve

Secret

Messages

0 0

0 00

0

0

0

0

0

0 0

0 00

0

0

0

0

0

Displayed

Image

DCT Coefficients DQT Quantized DCT

Coefficients

Secret

Messages

/ =

(b) The proposed JPEG Stego Model

Stego

Image

* =

DCT CoefficientsRaw

BMP/

TIFF Quantization Process Dequantization Process

0 0

0 00

0

0

0

0

0

Quantized DCT

Coefficients

00

Stego

Image

Embed Retrieve

0 0

0 00

0

0

0

0

0

0 0

0 00

0

0

0

0

0

Displayed

Image

,

Modify

DQT

Secret

Messages

Modified DQT

Figure 1. The normal (a) and the proposed (b) JPEG stego models.

Table 1. Compression effect using Irfan conversion tool [19]

0% (No compression) 20% 30% 50%

Lena.jpg

Baboon.jpg

ISBN: 978-0-9891305-2-3 ©2013 SDIWC 110

Figure 2. Encoding and decoding process of NFDV using private key system.

The third strategy is to embed more messages

beyond the red line of the chrominance DQT

table. The luminance component contributes the

most information to the image. Unlike the RGB

color model, where all components are roughly

equal, YCbCr concentrates the most important

information in one component. This makes it

possible to get greater compression by including

more data from the luminance component than

from the Cb and Cr components. Therefore,

modifying the chrominance DQT table of a cover

image even beyond the red line will give

insignificant distortion to its reconstructed stego

image. However, for the sake of higher

imperceptibility, only a few of the DQT values

beyond the red line will be changed. In the

following section, a complete stego-system using

NFDV is described.

3.1 A Stego System using NFDV

Figure 2 shows the complete encoding and

decoding phases of a private key stego-system

using NFDV. A sender, Bob, composes a message

M, chooses a cover image I and specifies a secret

key K. Feeding all of the above as inputs to our

NFDV encoder, a stego-image, I' is produced.

Bob then, send the secret key K and the stego-

image I' over two separate channels to the

receiver, Alice. Bob could send the secret key K

using text based steganography via an SMS (Short

Messaging System) message to Alice. At the

receiving end, Alice will feed the secret key K and

the stego-image I' to the NFDV decoder and get

back the original message M. The encoding

algorithm in Figure 2 is referred. Let us assume a

cover image I, a secret key K and a secret message

M are supplied to the encoding algorithm.

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Steps Tracing Values

1. Get the I, M and K M:FORCE

2. Calculate the size of M S M:FORCE; S:5

3. Using K, encrypt MM' M:FORCE; M':@#$%!; S:5

4. Using K, encrypt SS' M:FORCE; M':@#$%!; S:5; S’:32 (0x20)

5. Embed S' M:FORCE; M':@#$%!; S:5; S':32

5. Generate partial scatter code Q M:FORCE; M':@#$%!; S:5; S':32; Q:10110

6. MSB zero padding of QP M:FORCE; M':@#$%!; S:5; S':32; Q:10110

P:00010110 (0x16)

7. Using K, encrypt PP' M:FORCE; M':@#$%!; S:5; S':32; Q:10110

P:00010110 (0x16); P':00101010 (0x2A)

8. Embed P' M:FORCE; M':@#$%!; S:5; S':32; Q:10110

P:00010110 (0x16); P':00101010

9. Embed M' and calculate X M:FORCE; M':@#$%!; S:5; S’:32; Q:10110

P:00010110 (0x16); P':00101010; X:0x10

10. Using K, encrypt XX' M:FORCE; M':@#$%!; S:5; S':32; Q:10110

P:00010110 (0x16); P':00101010; X:0x10; X':0xBD

11. Embed X' M:FORCE; M':@#$%!; S:5; S':32; Q:10110

P:00010110 (0x16); P':00101010; X:0x10; X':0xBD

Other DQT values 0xBD ! ! % $ + # m @ 0x2A 0x20 SOF

X' M'5 M'4 M'3 M'2 M'1 P' S'

Figure 3. An example of embedding a message “FORCE” using NFDV

An example of embedding a message “FORCE”

using the NFDV technique is shown in Figure 3.

Initially, the size of the input message, S is

calculated. Using K, M and S is encrypted to get

M' and S', respectively. The message hiding

process proceeds if the message size is not more

than the maximum message size of 24 bytes.

Then, the encrypted form of the message size, S' is

embedded at the last byte of the DQT table. The P

series of 0’s and 1’s is generated (for the purpose

of scattering the message randomly in the DQT

table in the latter stage).

In this algorithm, a value 0 means no skip and

a value of 1 means skip 1 DQT byte. Once the

scatter-code P is generated, it is encrypted and

embedded in the next byte of the DQT table. For

example, if the message size is 5 bytes, and the

randomly generated code is 1, 0, 1, 1, 0, thus the

scatter-code is 10110 which implemented in

reverse that means not skip (0), skip (1), skip (1),

not skip (0) and finally skip (1). Since it is less

than a byte, M' is zero padded at its most

significant bits (MSBs). Therefore, the final

scatter-code is 0001 0110 or 0x16. After the S'

and P' have been embedded, the message parts of

M' is successively embedded according to the

scatter code P. During this process, the MIX (i.e

X) value is also calculated. Once all messages

have been embedded and the final MIX value has

been calculated, the MIX value, X is encrypted to

X' and embedded immediately after the last M'.

Notice that the skipped DQT elements are

replaced with random values. Encrypting and

scattering help protects against unauthorized

hidden message extraction [20]. Note that all

messages and its headers will be embedded into

the last DQT table prior to SOF using baseline

JPEG file. Baseline JPEG is used because it is the

most widely used JPEG in the Internet [17].

3.2 Message Integrity Checking Sequence

(MIX)

After the message is hidden, it is necessary to

append an integrity checker in order to detect any

tampering done. The message integrity checking

sequence (MIX) is a value that is produced from

calculating the CRC of a selective part of the

ISBN: 978-0-9891305-2-3 ©2013 SDIWC 112

encrypted message. The random scatter code is

used to decide which parts of the messages to be

considered in the CRC calculation. Furthermore, a

new CRC-8 polynomial known as 0xA6 =

12368 xxxx is chosen because it can

provide good Hemming Distance (HD) for

messages of less than 247 bits [21]. As a note,

only a maximum of 29 bytes of messages (232

bits) from the chrominance table are considered in

the proposed system. The CRC is a well known

technique used to detect bit modification to

messages sent over a network [22]. The choice of

the polynomials, polynomial lengths must be

suitable for the data size to avoid undetected

multibits errors. Baicheva et al. [22] and Koopman

and Chakravarty [21] provide extensive research

on choosing the best polynomial for different

applications. In short, the random feature of the

scatter code is used to further enhance the integrity

checking. The integrity checking mechanism is

illustrated in the following example.

Assuming the scatter code P is 0xAA and the

encrypted secret message M' is 0xAEEA BCDE

EEBB CCDE, based on P (0x10101010), the

data to be considered in the cyclic redundancy

check will be 0xEADE BBDE (from right -

first, third, fifth and seventh messages in M'.

Note that 0 mean “no skip”, 1 mean “skip”).

Using the chosen CRC-8 polynomials, we

calculate and obtain the CRC value of 0x6A.

Furthermore, after encryption with K, the MIX

(X) value will be embedded immediately after

the last byte of M' (i.e. after M'8). In short:

P M'1 M'3 M'5 M'7 X*

0xAA 0xEA 0xDE 0xBB 0xDE 0x6A

4 RESULTS AND DISCUSSION

The NFDV was developed using Matlab and C on

Windows XP operating system with Intel® dual-

core 1.8 MHz and 1GB memory. To allow

comparison with other techniques, standard JPEG

test images in steganography research domain are

used as the cover media. Furthermore, to see the

effect of the proposed technique on small images,

lena.jpg has been cropped into smaller images

to embed secret messages with the stego-system.

The following three types of experiments/

measurements were conducted:

4.1 Imperceptibility Test

Most researchers use Peak Signal to Noise Ratio

(PSNR) and Mean Square Error (MSE) to measure

the image quality [23]. The PSNR and MSE for

an NxN gray level image are defined as [24]:

dbMSE

PSNR2

10

255log.10 (1)

2

1 1

21

N

i

N

j

ijij XXN

MSE (2)

where:

ijX : The pixel values of the cover image.

ijX : The pixel values of the stego-image.

The PSNR of color images can be calculated

by first evaluating the MSE values for every color

channel separately and then, taken the sum of the

MSE values, MSE'. To calculate the PSNR, the

peak value (MSE

2255) is then replaced with

(MSE'

3*2552

).

The results of these tests are illustrated in

Table 2 and Table 3. Notes that “PSNR = INF”

indicates that the cover image used and the stego

image produced are identical. It is shown in the

experiment that image quality very slowly

degrades as the message capacity increases (refer

to Table 2), and rapidly deteriorates if the message

is bigger than 24 bytes. Therefore, although more

messages can be fitted into the DQT, a maximum

message size (message capacity) of 24 bytes is

recommended to get very low image distortion. If

the message size is 24, the scatter-code will be

using 3 bytes of the AC values in DQT. Therefore,

the total DQT values needed would be

1+3+24+24+1=53 bytes, which is just fine before

it rapidly distort the image.

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Table 2. The visual imperceptibility of the stego images with different message size and compression ratio.

Message Size

(in bytes)

Steganography Image

(lena.jpg with 30% compression)

Steganography Image (baboon.jpg

with 20% compression)

5

PSNR = INF

PSNR = INF

15

PSNR = INF

PSNR = 50.70

24

PSNR = 49.68

PSNR = 42.34

27

PSNR = 20.72

PSNR = 19.52

30 Image corrupted Image corrupted

The steganography image cannot be viewed at all

when the message size is 30 bytes as it overrides

the DC value. However, by considering that

several 0s will turn up in the scatter code, it may

be possible to embed more messages. As

demonstrated in Table 3, it appears that using

small images seems to produce better PSNR. It

may be due to the lesser chrominance DCT

coefficients are affected by the embedding

process, as compared to larger images.

Furthermore, as demonstrated in Table 2,

using higher compression JPEG images as its

hosts, the size of the NFDV area gets enlarged and

therefore able to store more data without

sacrificing the imperceptibility of the compressed

images.

Finally, a comparison of PSNR values with

other methods is given in Table 4. It shows the

PSNR of different methods when all the available

pools in the cover images are used to embed secret

data. From the results, it is shown that the

imperceptibility of stego-images produced by the

proposed system exceeded previous PSNR values

reported in previous literature.

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Table 3. The imperceptibility of small images with 24 bytes of secret messages in the chrominance DQT.

Table 4. The quality of stego-images using different schemes

ISBN: 978-0-9891305-2-3 ©2013 SDIWC 115

(a)

Steganalysis Method Tools Detection Outcome

Statistical Chi-Square Attack [26] StegDetect 0.6 Fail

Statistical RS Analysis [27] Virtual Steganography Laboratory [28] Fail

Linear Discriminant Analysis [29] StegDetect 0.6 Fail

(b)

Figure 4. Steganalysis Result. (a) The histogram of the lena.jpg cover image (left) and its stego image (right). Other images with

different amount of secret messages produce similar result. (b) Results of performing some statistical or anomaly based

steganalysis methods on Lena and Baboon stego images with 24 bytes of messages.

4.2 Image Histogram and Steganalysis Test

Interestingly, Figure 4(a) shows that NFDV does

not change the histogram color information of the

image if embedding is done below the red line.

Therefore, it is expected that the pixel histogram

analysis based attacks will not be able to detect the

existence of secret messages in the stego-images.

Obviously, this is due to the fact that DQT does

not hold any information about pixel or pixel

relations. It is simply used to quantize DCT

coefficients to get greater number of zeros for

compression purpose.

The stego-images are also evaluated based on

its ability to persist detection from several well-

known steganalysis techniques and tools,

especially against statistical/anomaly attacks. Two

of the attacks are based on first order statistical

attack (Chi-square and RS analysis).

Another one is based on second order

statistical attack (linear discriminant analysis).

Figure 4(b) illustrates the outcome of the

steganalysis attacks. Stegdetect and Stegbreak

[25] employ specific signature attack, Chi-square

attack and also dictionary attack. They cannot

detect the existence of secret messages in the stego

images as they can only handle specific schemes

such as jsteg, jphide (unix and windows), invisible

secrets, outguess 01.3b, F5, appendX and

camouflage. The Chi-square attack built into

Stegdetect was also not able to detect secret

messages in the stego-images as the modifications

are not affecting the DCTs. Only a few of the

chrominance DCTs are affected due to embedding

ISBN: 978-0-9891305-2-3 ©2013 SDIWC 116

beyond the red line (Figure 1). Furthermore,

without appropriate key, message length and

locations are unknown in the proposed stego-

system. Due to these limitations, detection and

retrieval of the secret messages from the stego-

image is difficult, if not possible.

4.3 Integrity and Confidentiality Test

In this experiment, two stego-images with

message sizes of 5 and 24 bytes each have been

used. Modification is made to DCT values that

carry messages and also not carrying any

messages. Results obtained from the integrity test

shows that the integrity checker in NFDV is able

to detect modified message bytes but unable to

detect modified non-message byte. It indicates that

the MIX is affected only by tampering of data in

the message byte. Therefore, even if the

steganography images are corrupted, the hidden

message can still be retrieved as long as the

message bytes are not altered. The message

integrity checking sequence is highly sensitive to

changes in bits of the messages as it takes into

account the summation, the position of the bit

value and also the scatter code in its calculation.

5 CONCLUSION

In this paper, a data hiding scheme for highly

compressed JPEG has been proposed. The scheme

produces highly imperceptible stego-images that

are effective against a group of statistical

histogram steganalysis attacks, such as Chi-

square, RS analysis and linear discriminant

analysis attacks. Using higher JPEG compression

ratio, similar to the ones usually communicated on

the Internet, more secret messages can be

embedded without changing the pixel’s statistical

property. We have demonstrated a reasonably

complex stego-system using the NFDV technique.

The technique can also be used for sending secret

messages disseminated via groups of small JPEG

images, such as through a web site or a document

consisting of small photos, image clips,

advertising photo images and artworks in JPEG

format.

6 ACKNOWLEDGEMENTS

This work is supported by Universiti Tun Hussein

Onn Malaysia. The grant number is UTHM-

FRGS-1051.

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