Hybrid of Least Significant Bits and most Significant Bits for
Improving Security and Quality of Digital Image Steganography
ABUBAKAR AMINU MU’AZU, KAUTHAR KABIR*
Department of Computer Science, Umaru Musa Yaradua University,
Katsina-Nigeria, Dutsin-ma Road, P.M.B 2218,
NIGERIA
*Corresponding Author
Abstract: - The Security of confidential communication is protected using the most popular type of carrier to
hold information known as Image steganography. The Least Significant Bit (LSB) algorithm and the Most
Significant Bit (MSB) algorithm are steganography algorithms used for information hiding in digital images
both have disadvantages of Low image quality, Security, vulnerability to any small modifications, and long
encoding time during message compression. To overcome this limitation, the research proposes a Secured
Hybrid algorithm called S-Hybrid to combine (LSB and MSB) bits based on checking Two bits (the least
significant bit and the most significant bit) of the cover images and replace them with a secret message which
was implemented in Netbeans IDE. However, the S-Hybrid algorithm produced the best stego-image quality.
Large cover images made the hybrid algorithm’s quality better. The proposed S-Hybrid had a lesser encoding
time than the existing method having the highest compression ratio which reduces the transmission effort
making the encoding time short which is correlated to the security and makes the proposed method perform
better than the existing one. Therefore, a trade-off exists between the encoding time and the quality of stego-
image as demonstrated in this work. Mean-squared error (MSE), Peak signal-to-noise ratio (PSNR), encoding
time, and Compression ratio are used for performance evaluation between the proposed S-Hybrid algorithm and
the existing Method after embedding messages in digital images.
Key-Words: - Cover images, Least Significant Bit (LSB), Most Significant Bit (MSB), Steganography, Mean-
squared error (MSE), Peak signal-to-noise ratio (PSNR), S-Hybrid algorithm.
Received: July 19, 2023. Revised: October 9, 2023. Accepted: November 14, 2023. Published: December 12, 2023.
1 Introduction
Steganography is the skill and science of hiding
delicate info in means that prevent revealing. The
purpose of steganography is to deliver a message in
such a way that not one person apart from the
sender and projected recipient suspects the
presence of the message. These messages are
transported through cover objects such as text,
audio, images, and protocols. The top-secret
message could be plaintext, cipher text, or images.
The embedding of the message into a cover object
results in a stego-image. The study, [1], reported
that the Most significant bit (MSB ) is the highest
bit in a series of numbers in binary. e.g. in the
binary number: 11001100, the most significant bit
is far left 1. In the MSB technique, the secret
message is embedded in the most significant bit of
the pixel of the image.mages are typically used as
shield objects in steganography, [2], [3].
The method of Image steganography is
classified into two categories based on the working
domain: Spatial domain where the pixel value is
directly modified for data hiding. Images in this
domain are represented as a rectangular grid of
pixels or points of color where the human
perception does not observe the image as a grid.
LSB and MSB known as Least significant bit and
Most significant bit respectively -based hiding
strategies are most commonly used in this
approach. Frequency domain where the Images that
are in the domain of transform space are more
robust in terms of some image processing
manipulation and lossy compression and less prone
to attacks Discrete cosine transform-based
steganography and Discrete wavelet transform-
based steganography are most commonly used in
this approach.
Parameters such as imperceptibility,
robustness, and capacity can be used to measure the
performance of a steganography technique.
Imperceptibility is the capacity to avoid
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recognition, i.e. the failure to define the presence of
a concealed message. This makes it a significant
prerequisite in steganography. Robustness is how
fine a steganography method can battle the
withdrawal of hidden data. It measures the
capability of the steganography method to persist in
the efforts of removing the hidden information.
Such challenges include image manipulation (like
cropping or rotating), data compression, and image
filtering. Payload Capacity signifies the extreme
amount of information that can be securely
embedded and retrieved in a work without being
statistically noticeable. When likened to
watermarking which requires embedding only a
minor amount of right information, Steganography
requires adequate embedding capability.
Least Significant Bit (LSB) Replacement is an
embedding method based on the fact that the least
significant bits in an image can be thought of as
random noise, and consequently, they become not
responsive to any change in the image. The secret
message is hidden by altering the least significant
bit in a certain layer of the image file. This change
is so slight that the human eye may not notice it.
The Hybrid LSB - MSB algorithm is a
combination of the aforementioned algorithms. It
works by combining the two techniques, LSB and
MSB into a hybrid algorithm that embeds the top-
secret message bits into the least significant bit of
and the most significant bit of the cover image.
This work aims to Enhance the combination of
LSB-MSB algorithms into a secured hybrid
approach for digital image quality steganography.
The objectives of this research are:
1. To develop a secured hybrid algorithm for
encoding secret message bits into the least
significant bit and most significant bit of the
cover image based on checking two bits LSB
and MSB and replacing them with the secret
message bits.
2. To implement the secured hybrid algorithm
having an encoding interface and a decoding
interface for hiding and retrieving purposes
respectively.
3. To evaluate the performance of the proposed
algorithm and compare it with existing
Methods using different image formats and the
quality of the image with an increase in file
size.
The continuing part of the paper is structured as
follows. Subdivision 2 works on current image
steganography methods and subdivision 3 presents
the projected image embedding method. In
Subdivision 4 the experimental outcomes &
conversation are shown and conclusions in
Subdivision 5.
2 Related Works
The security of information during transmission in
the open network is crucial. While sharing digital
data on the internet, it is essential to observe
information security goals: confidentiality,
integrity, authenticity, and accuracy, [1]. The study,
[2], highlights the Issues due to limitations in color
variations and the use of a color map in
steganography utilizing the LSB technique to
embed data in the 8-bit color image using Secret
Key implementation and successfully embed data
in the 8-bit color image because during its
implementation, after the process of compression, a
text message is hidden in the final, compressed
image. It eases up the exchange of information in
different forms, be it text, image, audio, video, or
other formats; however, it becomes a challenge to
secure the data during transmission in the open
network, [3].The study, [4], works on insecurity in
the transmission of confidential information using
an Arithmetic coding algorithm in MATLAB to
increase imperceptibility in stereo image PSNR
MSE SSIM and HISTOGRAM for evaluation,
needs improvement in PSNR and latch mechanism
for selecting hidden bits’ similar secret bits in RGB
image.This hidden information can be retrieved
only through proper decoding techniques.
Unfortunately, it lacks features of support for file
types other than bit maps. Authors of, [5], use an
approach of the STC framework to implement an
Algorithm of cost assignment according to the
characteristics of GIF images and payload
allocation algorithm for different frames. Use the
motion difference of adjacent frames as an adjacent
factor as a performance measure metric and Point
out that Security is the major concern in
Steganography.The study [6], implemented a
Modified LSB Technique using a cryptography
approach and tested the performance of the work
with MSE and PSNR solving the issue of secrecy
of data and cyber-crimes. The study, [7], proposed
data hiding with 1 bpp, 2 bpp,3 bpp, and Variable
embedding capacity, using three approaches K-bit
LSB replacement, LSB matching revised and XOR-
based data hiding method, and testing the work
using Image analysis and Histogram analysis.
Finally highlighted that the Security problem still
needs to be solved.
Solve problems associated with effective and
robust image security using a framework
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particularly designed for the images using a hybrid
image security approach implemented in
MATLAB, the proposed algorithm is designed to
fill that certain gap of stronger security mechanism
for image sharing-based social media applications.
Elapsed time, MSE, PSNR, and Compression ratio
were the metrics used and the system does not work
on different types of datasets of images or other
types of digital media, [8]. The study, [9], solved
Information hiding capacity in steganography using
Novel LSB with the strength of Using coding to
increase steganographic capacity method and
weakness of no idea for hiding messages with a
large number of bits, MSE, PSNR, capacity, and
SSIM are used for evaluation. The study, [10],
addressed the problem of data and information
hiding in digital images using the Bitmap
Steganography technique under the implementation
called Any file can be hidden in an image, can take
any type of image file without converting it to
bitmap and using maximum memory space to hide
files in pictures. Just an attempt to identify
steganography techniques, [11]. The difficulty with
confidentiality in symmetric cryptography is that,
as we all know, a secret key is used to both convert
and decode the communication. As a result, this
key must be interchanged by both communication
parties in some way, or they must depend on a third
organization, such as a key allocation center, to
allocate the key. However, depending on a third
organization jeopardizes the secret key's
confidentiality. In public key cryptography, each
user must produce a pair of keys, one of which is
kept hidden and is known as a private key, while
the other is made public and is known as a public
key.
The study, [12], presented the problem of data
hiding in LSB thus utilizing the MSB for check,
using the method of checking MSB values and
replacing bits from LSB with secret messages,
which was implemented in C visual studio. The
proposed approach gives a better evaluation value
and is more secure so that a hacker cannot estimate
the pixel location and how to embed data by using
LSB and MSB bits. MSE, PSNR, Payload, and
Histogram were used as evaluation metrics, the
security is weak because the information is hidden
in only LSB abandoning the MSB part, and the
encoding time issue is not addressed at all.
3 Proposed an S-Hybrid Algorithm
The S-hybrid algorithm is a combination of the
MSB and the LSB algorithms. It pulls on the very
best features of the earlier examined algorithms. It
works by combining the LSB and the MSB
techniques into a secured hybrid algorithm that
embeds the secret message bits into the least
significant bit and the most significant bit of the
cover image. The goal of this method is to preserve
the statistical and visual features of the cover image
and obtain a better stego-image that solves the
security issues which include the application of
certain operations like cropping, and resizing by an
unknown party to detect the hidden message not
intended for them, image quality and long encoding
time issues associated with information hiding in
digital images. In the proposed system the secret
message is used to hide in a cover jpg or png
image. Firstly, the Cover image is broken into
minor parts, say 8x8 pixels, working from left to
right, top to bottom, the DWT is applied to each
block and each block is compressed through
quantization. Each character of the secret message
and each pixel of the cover jpg or png image are
converted to binary values. The user has to input
the stego-key as the password (the stego-key is
used to embed the secret message in a cover file).
After embedding the secret message into to cover
image file, the resulting end is the stego-image,
while defining the starting point of embedding in
LSB and MSB to enhance the security, the
summation of the ASCII value of each character of
the stego-key is calculated and then the average of
those characters value is computed, substituting the
secret message into the LSB and MSB of the cover
image. The first LSB and MSB positions are
chosen according to the calculated average value of
the input stereo-key characters, the substitution
processing will continue until the end of the secret
message.
An illustration of message embedding and
extraction using the proposed algorithm
The proposed S-Hybrid algorithm embeds the
secret text in LSB and MSB. It takes two bits of
secret text and hides the first bit in LSB and the
second bit in the MSB. The research considers an
RGB 24 jpg and PNG image.
Data to be inserted: character ‘A’: 01000001
Pixels are used to store one character of 8 bits.
Embedding ‘A’
Cover Image:
00100111 11101001 11001000
00100111 11001000 11101001
11001000 00100111 11101001
S-Hybrid: 00100111 01101000 01001000
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00100111 11001000 11101001
11001000 00100111 11101001
Extracting ‘A’=> 0100000
Fig. 1: LSB and MSB for embedding and extraction algorithm for three colors.
Fig. 2: Proposed Architectural Model
The Architectural Model designed for this
research (Figure 2) is one of the key items of the
research, From Figure 1, when the cover image and
the secret text message have been carefully chosen,
the embedding stage of the hybrid algorithm picks
two bits of the secret message and embeds the first
message bit in the least significant bit of the cover
image byte and the second message bit in the most
Hide 3rd and 4th message
Bits
Red Green Blue
7
6
5
3
2
1
0
7
6
5
4
3
2
1
0
7
6
5
4
3
2
1
0
Hide 1st and 2nd message Hide 5th and 6th bits
Bits in the MSB (7) and
LSB (0)
7
6
5
3
2
1
0
7
6
5
4
3
2
1
0
7
6
5
4
3
2
1
0
Hide 7th and 8th message bit
(a) Phase 1: Embedding (RGB)
0 1 0 0 0 0
7
6
5
3
2
1
0
7
6
5
4
3
2
1
0
7
6
5
4
3
2
1
0
0 1
(b) Phase 2: Extraction (RGB)
7
6
5
3
2
1
0
7
6
5
4
3
2
1
0
7
6
5
4
3
2
1
0
Phase One
Cover Image Stego-image
DWT
Convert to byte array
Phase Two
Key
Secret text message
S-Hybrid
(Embedding Stage)
Secret text
message and
Cover image
S-Hybrid
(Extraction Stage)
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significant bit of the cover image byte, the Key is
the secret key utilized in the embedding stage
increasing security. The retrieving stage is the
opposite of the embedding stage.
3.1 Algorithm for the Proposed Secured
Hybrid LSB-MSB
The Embedding algorithm is as follows;
Input: An M×N size cover image and message to
be hidden.
Output: Stego image.
Step 1: Image img = get image
Step 2: Let n = width (img)
m = length (img)
Step 3: Let x be message to hide
Begin
Step 4: The image is broken into minor parts, say
8x8 pixels, working from left to right, top to
bottom, the DWT is applied to each block and each
block is compressed through quantization.
Step 5: Convert the cover image using the read()
method of the image IO class into
ByteArrayoutputstream
Step 6: Convert Message character text(x) string
into Byte Array using string.getBytes()
Step 7: Accept the stegeo-key from the user and
calculate their average value of them.
Step 8: Convert each character of the secret
message and each LSB and MSB of the cover
image from a position of an average of stegeo-key.
Step 9: If the image cannot contain the message
exit with an error message
Else
for each bit in the message byte
Begin
Step 10: If using hybrid LSB-MSB (proposed
algorithm), get two message(x) bits and hide the
first message(x) bit in the MSB of the
corresponding cover image byte and the second
message(x) bit in the LSB of the corresponding
cover image byte.
Step 11: End
End
The Extraction algorithm is as follows;
Input: Stego-image
Output: Cover image and message.
Step 1: Begin
Step 2: Input Stego-image
Step 3: Convert stego image using read() method of
image IO class into ByteArrayoutputstream
Step 4: If the decoding type is LSB-MSB
Step 5: Begin
Step 6: for the first 32 bytes, copy the LSB and
MSB into an array of 32
Step 7: Using the int attribute convert the array into
an integer value
Step 8: Create an array of lengths of the integer
value
Step 9: Starting from length 32+1 of the stego-
image array
Step 10: Begin
Step 11: Copy the LSB and MSB of the equivalent
stego array into an array of length 8.
Step 12: Convert the array into a byte value and
save it in the corresponding index of the created
array
Step 13: Convert the array value into a string or
image
Step 14: Display msg and cover image
Step 15: End
End
3.2 Format of the File
Any image file design can be used equally as the
cover image. However, the image was first
transformed into PNG format before something
could be done on it. After the entire procedure, the
image was transformed back to its unique design.
PNG format is chosen because it is sustained by the
Java image IO library; it applies a lossless file
density method and allows for easy exchange and
observation of image data stored on local or
isolated computer systems. Also, it appears to
preserve a high point of image quality after the
message has been embedded.
3.3 Performance Evaluation Metrics
Analyzing the image quality and security for
proposed and existing work will be done using four
parameters of analysis which include;
i. Mean-Squared Error
ii. Peak Signal-to-Noise ratio(PSNR)
iii. Encoding Time
iv. Compression Ratio
i. Mean Squared Error: This is defined to
measure the distortion of the image which is the
difference of error between the original and stego
image. The less the Mean Squared Error, the better
the image quality. To calculate the MSE between
two images I1(M,N) and I2(M,N);
MSE= ∑
NM
NMINMI
*
2))],(),([( 21
(1)
Where M and N are the number of rows and
columns in the input images respectively
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ii. Peak Signal-to-Noise Ratio(PSNR): The
Peak signal-to-noise ratio is used to compare the
image compression quality of the original image
and stego image. If PSNR is 40dB or greater, the
original and reconstructed images are usually
indistinguishable by human observers.
PSNR = (2)
Where the 255 here is the value as substituted
for R
However, the lower the MSE value and the
higher the PSNR value the better the quality of the
image.
iii. Encoding time: Encoding time refers to the
period it takes to embed a secret text message in an
image. Processing image security mechanism had
to be effective in terms of encoding time. If Time T
is a variable and Data size D is another variable,
then the rate of change of T with respect to D is
given by
dT/dD. (3)
iv. Compression ratio: represents the reduction in
the size of the image after the compression process.
The higher the compression ratio; the lower the
transmission effort and disk space consumption.
Size of the original image
A = (Width * Height * Number of Color planes *
bit depth)/8 bytes
B = Size of compression image = size_ in_bytes
Compression ratio = A:
B
(4)
4 Simulated Results
A simple system was developed to implement the
proposed Secured Hybrid LSB-MSB algorithm
using JAVA programming language. There are two
sides to the system, the embedding interface and
the Extraction interface for hiding and extraction
purposes respectively. We tested the system using
two different images: rose.jpg, and giraffe.png as
cover images. We have established that the
encoding time is correlated to the security of the
algorithm in question. A 30.4 kilo-byte document
was also used as the message text.
To evaluate the performance of the proposed
method, we use two images Rose.jpg and
Giraffe.png for message embedding. Table 1 shows
the results of the experiment using two jpeg and
png images and their respective dimensions, file
size, and the text size that had been added
‘steganographically’.
Fig. 3: (560 x 448 pixels rose.jpg ):
(I) Original image (II) Stego-image using S-Hybrid
Using roses.jpg with dimensions 560 x 448
pixels as the cover image and a 30.4-kilo byte
document as the message, it can be seen from
image II of Figure 3 show noticeable differences
when compared to the original cover image.
Increasing the dimension of roses.jpg to 5040 x
4032 pixels to improve the image quality of the
proposed algorithm, the payload capacity increases
for the proposed algorithm (Figure 3 (II)).
Therefore, the larger the cover image the more data
that can be stored.
Fig. 4: (5040 x 4032 pixels rose.jpg ):
(I) Original Image (II) Stego-image using S-hybrid
Figure 4 shows the 5040 x 4032 pixels rose.jpg
hiding an image. Figure 5 shows the output of the
newly created stego-images after hiding text with a
file size of 30.4kb (31,160 bytes) in an image in
MSE
2
10
)255(
log10
I II
I II
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PNG format. The dimension of the cover image,
giraffe.png is 750 x 1125 pixels. Figure 5 (II)
showed a noticeable difference when compared to
the original cover image after embedding text. The
differences are noticeable in the top sections of
Figure 5 (I) and (II).
Fig. 5: (750 x 1125 pixels giraffe.png):
(I) Original image (II) Stego-image S-Hybrid
Increasing the dimension of the PNG file to
6750 x 10125 pixels for quality enhancement,
produced a stego-image indistinguishable from the
original cover image when viewed with the human
eyes for the proposed algorithms (Figure 6 II).
Fig. 6: (6750 x 10125 pixels giraffe.png hiding
text): (I) Original Image (II) Stego-image using S-
hybrid
Table 1 represents the Simulation results of
PSNR and MSE for the proposed method by
experimenting with Four images and different
image formats and dimensions of the image and
inserting a text message of 30.4 kilobytes.
Table 1. Simulation results of PSNR and MSE for
an S-Hybrid
Cover
Image
Size of the
Image
PSNR
MSE
Rose.jpg
560 x 448
79.273
0.00010
Rose.jpg
5040 x 4032
80.256
0.00025
Giraffe.png
750 x 1125
81.390
0.00020
Giraffe.png
6770 x10125
50.31
0.061
Here PSNR and MSE are calculated as
follows:
MSE = ∑
NM
NMINMI
*
2))],(),([( 21
(5)
PSNR = (6)
Where the 255 here is the value as substituted for R
Table 2 represents the Simulation result of
Encoding for the proposed method by
experimenting with Four images and different
image formats and the dimensions of the image and
inserting a text message of 30.4 kilobytes.
Table 2. Simulation result of Encoding for an S-
Hybrid
Cover Image
Size of the Image
Encoding
Time(ms)
Rose.jpg
560 x 448
192
Rose.jpg
5040 x 4032
190
Giraffe.png
750 x 1125
198
Giraffe.png
6770 x10125
194
Here Encoding Time(ms) is calculated as follows:
If Time T is a variable and Data size D is another
variable, then the rate of change of T with respect
to D is given by:
dT/dD (7)
Table 3 represents the Simulation result of the
Compression ratio for the proposed method by
experimenting with Four images and different
image formats and dimensions of the image and
inserting a text message of 30.4 kilobytes.
Table 3. Simulation result of the Compression ratio
for an S-Hybrid
Cover
Image
Size of the Image
Compression
Ratio
Rose.jpg
560 x 448
38.96
Rose.jpg
5040 x 4032
43.43
Giraffe.png
750 x 1125
42.71
Giraffe.png
6770 x10125
43.46
Here Compression Ratio is calculated as follows:
Size of the original image
A = (Width * Height * Number of Color
planes * bit depth)/8 bytes
B = Size of compression image = size_
in_bytes
Compression ratio = A: B (8)
I II
I II
MSE
2
10
)255(
log10
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Table 4. Values of MSEs, and PSNRs for the existing (Mahdi method) and S-hybrid (proposed) algorithm
Table 4 shows the MSE and PSNR of the
cover image text embedding. It can be seen that a
lower MSE value and a higher PSNR value for the
proposed S-Hybrid algorithms for text were
obtained. This results in better image quality since
the lower the MSE value and the higher the PSNR
value, the better the quality of the image, and hence
imperceptibility is improved. The last evaluation is
a histogram showing the comparisons between the
cover image and stego image using Rose.jpg and
Giraffe.png of increasing dimensions as shown in
Figure 7 which is the resulting stego image same as
the histogram of the cover image.
Fig. 7: Cover image and Stegeo image comparison Histogram
Cover image
Rose.jpg and
Giraffe.png
Histogram Stego-image
Rose.jpg and Giraffe.png
Histogram cover image
Rose.jpg and Giraffe.png
S/N
Cover Image
Algorithm
Message Image (31,160 Bytes, 30.4KB)
Dimension
File Size
PSNR (db)
MSE (db)
1
Figure 3 560 x 448
pixels rose.jpg
96.3 KB
Proposed S-Hybrid
79.273
0.00010
Mahdi method
(Existing)
87.141
0.00012
Figure 4 (5040 x
4032 pixels rose.jpg
2.14 MB
Proposed S-Hybrid
80.256
0.00025
Mahdi method
(Existing)
83.742
0.00027
2
Figure 5 (750
1125 pixels
giraffe.png
1.2 MB
Proposed
S-Hybrid
81.390
0.00020
Mahdi method
(Existing)
84.608
0.00022
Figure 6 (6750 x
10125 pixels
giraffe.png
37.0MB
Proposed
S-Hybrid
89.23
0.0002
Mahdi method
(Existing)
87.35
0.0001
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5 Conclusion and Future Work
The proposed method gives better performance in
all the parameters than the existing one. The stego
image generated after embedding the secret
message in the cover image is almost identical to
the original image. However, when the sizes of the
cover images were increased, the image quality of
the proposed algorithm increased, which means that
the larger the cover image, the better the hiding
capacity. Compression Ratio represents the
reduction in the size of the image after the
compression process. The higher the compression
ratio; the lower the transmission effort and disk
space consumption. In the case of the proposed
algorithm, the compression is recorded way higher.
The encoding times of the proposed Secured
Hybrid algorithm for various sizes of the different
images were lesser and the security is better
because its complex coding is what makes the
proposed algorithm better secured. The proposed
scheme is developed to ensure more image security
during transmission by facilitating quick image
transfers. Also, the processing image security
mechanism had to be effective in terms of encoding
time. The results of the proposed algorithm have
shown that the proposed algorithm has performed
stronger and lossless compression on the images.
The overall system performance has shown that the
new system is robust, quick, and effective for
image security.
5.1 Future Work
In the future, more work can be done to look into
improving the Secured hybrid LSB - MSB
steganography algorithm for increased efficiency
without compromising data security or image
quality. This can be achieved by working on the
compression ratio for stronger embedding
procedures and also finding a way to apply this
technique on larger-sized gray-scale images. The
proposed technique in the future will be extended
to use on other steganographic cover objects such
as video or audio.
Additionally, integrating machine learning into
steganography presents a promising avenue to both
detect and prevent steganographic activities while
enhancing the overall security and quality of digital
image steganography.
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WSEAS TRANSACTIONS on COMPUTERS
DOI: 10.37394/23205.2023.22.29
Abubakar Aminu Mu’azu, Kauthar Kabir
E-ISSN: 2224-2872
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Volume 22, 2023
Matching Revisited, Security and
Communication Networks, Vol. 2021,
Article ID 6610678, 15,
https://doi.org/10.1155/2021/6610678.
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Conflict of Interest
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DOI: 10.37394/23205.2023.22.29
Abubakar Aminu Mu’azu, Kauthar Kabir
E-ISSN: 2224-2872
262
Volume 22, 2023