Developing Algorithms for Image Steganography and Increasing the Capacity Depending on Choosing the Best Pixels

International Journal of Computer Networks & Communications (IJCNC) Vol.14, No.4, July 2022
DOI: 10.5121/ijcnc.2022.14406 99
DEVELOPING ALGORITHMS FOR IMAGE
STEGANOGRAPHY AND INCREASING THE CAPACITY
DEPENDING ON CHOOSING THE BEST PIXELS
Mayar khaled1
and Ahmed H. Abu El-Atta2
1
Mathematics Department, Faculty of Science, Benha University, Egypt
2
Computer Science department, Faculty of Computers and artificial intelligence,
Benha University, Egypt
ABSTRACT
Steganography is a vital technique for transferring confidential information via an insecure network. In
addition, digital images are used as a cover to communicate sensitive information. The Least Significant
Bit (LSB) method is one of the simplest ways to insert secret data into a cover image. In this paper, the
secret text is compressed twice by an Arithmetic coding algorithm, and the resulting secret bits are hidden
in the cover pixels of the image corresponding to the pixels of each of the following three methods, one of
three methods is used in each experiment: The first method, the edges of the image are modified to increase
the number of edges, in the second method the lighter-colored regions are selected, and in the third
method, the two methods are combined together to increase security and keep the secret message
unrecognized. Hiding in each of the previous methods is done by using the LSB technique in the last 2-bit.
The correction approach is used to increase the stego image's imperceptibility. The experimental results
show that with an average message size of 29.8 kb, the average Peak Signal-to-Noise Ratio (PSNR) for the
second proposed (Light regions) method equals 62.76 dB and for the third proposed (Edge and region)
method equals 62.72 dB, which is a reasonable result when compared to other steganographic techniques.
KEYWORDS
Steganography, LSB technique, Edge detector, light regions, Arithmetic coding, Correction method.
1. INTRODUCTION
In today’s world, the requirement for secure communication for the sharing of information has
long been recognized in the networked system. With this, a secure channel with good security is
required so that a third party cannot access one's sensitive data. The safeguarding of confidential
information is the major concern of most security systems. The three most popular strategies for
protecting information are watermarking, steganography and cryptography are all commonly
employed [1].
Cryptography is the study of concealing information by converting it into an unreadable format
through the use of encryption techniques. Watermarking is a method for copyrighting one's
property by incorporating a watermark that detects counterfeit measures. Steganography, on the
other hand, is a method of concealing data and information in a variety of file kinds, including
images, video, text, and audio files as shown in fig.1 [2].

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Figure 1. Classification of information hiding.
Steganography is a Greek word that means "covered writing" with "stegano" referring to "cover
or concealment" and "graphic" referring to "writing" [3]. When employing images as cover
material, you can choose between using the spatial domain or the transform domain [4]. The
spatial domain strategies are straightforward to comprehend and apply. The technique involves
Least Significant Bit (LSB), Histogram Shifting, Pixel Value Differencing (PVD), Multiple Bit-
planes-based, Expansion-based, Quantization-based, Palette-based, Steganography based on
Pattern-based, and Pixel Intensity Modulation. The LSB is one of the most widely utilized and
well-known spatial domain image steganography algorithms. Based on the image histogram,
Kamal and Islam [5] proposed distributing pixel values and grouping them into pixel groups. To
increase payload capacity, they use prediction mistakes. Then they apply prediction to the most
recent absolute-valued mistakes calculated. This increases the amount of data that can be stored
in the image, but it degrades the image quality dramatically. Maniriho and Ahmad introduced a
new data concealment method based on difference expansion (DE) [6]. Secret data can be hidden
in both positive and negative differences obtained between pixels using the proposed method.
This method has a good PSNR of 55 dB value and lower capacity.
The confidential bits are hidden under the sub-band frequency coefficients in transform domain
techniques. The embedding and decoding procedure in the transform domain is more complex
than the techniques employed in the time domain. The Discrete Fourier Transform (DFT),
Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), Complex wavelet
transforms (CWT), Integer Wavelet Transform (IWT), Compressive sensing (CS), Dual-Tree
Complex wavelet transforms (DTCWT), are examples of transform technique. Emad et al. [7]
presented a steganography approach that uses LSB substitution to hide hidden text in the
approximation band of the IWT of the cover image. It is permitted to have a low complete
payload capacity of 8192 digits. PSNR is decreased as a result of embedding in approximation
coefficients.
Imperceptibility, capacity, and robustness are the three properties of steganography [8]. The
capacity is determined by the number of hidden bits embedded in each cover pixel. More
classified data might be inserted in the cover image if the capacity was bigger. When determining
imperceptibility, the PSNR is commonly utilized. The better the stego image quality, the higher
the PSNR score. Personal data can be hacked or stolen if the system is not robust.
In previous works, the secret message that is hidden on the image's edges has a smaller effect on
the cover image than if it had been implemented in the direct pixels. The hiding inside the edges
is one of the strongest aspects of the image steganography, but when only using the image's edges
pixels, the capability of hiding more data is reduced due to the small number of the edges' pixels
in the single image. For this reason, we aim to increase the capacity of the secret data that we can
hide by choosing the cover pixels that achieve the benefits of hiding data that are similar to the

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edges' pixels. Also, we focus on applying ways to reduce the difference between the cover image
and the stego-image to maintain the confidentiality of information.
The body of this paper is organized as follows: Section 2 describes the related work to this study.
The fundamentals of the proposed combination schemes are described in section 3. The
simulation results and discussions are shown in section 4. The conclusion of the paper is
presented in section 5.
2. RELATED WORKS
Many researchers in the field of steganography use the LSB approach, edges' pixels, and other
techniques to hide information in images. Using a random methodology and a basic hash
function, a novel approach for hiding a secret text in a grayscale image was proposed by E.
Abbood et al. [9]. A pseudo-random number generator is used in each row to calculate the
location of the columns required to hide hidden text using the LSB approach. This approach had a
low PSNR of 55.3 dB because the authors did not choose the best pixels in the hiding process.
A method based on the LSB method is proposed in the study [10] to improve the payload by
hiding messages on the constricted edge areas. To provide security, messages are encrypted via
XOR operations with the Most Significant Bit rather than being placed directly on the LSB. This
method affords a low embedding rate where the maximum number of edge pixels is 14487 pixels.
The research [11] offers a steganography approach based on deep learning for concealing hidden
information within the cover image. Use a convolutional neural network (CNN) with a Deep
Supervision-based edge detector to do this, which can preserve more edge pixels than traditional
edge detection algorithms. But the PSNR value was low at 49.61 dB.
In [12], Setiadi et al. suggested a hybrid Canny-Sobel edge detection for improved LSB image
steganography. The pixels were hidden in the edge areas, which a hybrid Canny-Sobel detector
discovered. The approach, on the other hand, had a smaller payload capacity.
Based on edge detection, Lee et al. introduced a data hiding strategy with large embedding
capacity and great visual quality [13]. The edge map of the 4-MSB plane of the original image
was detected using the edge detection method based on the Maximizing Objective Function (ED-
MOF). The LSB plane had a hidden message embedded in it. This approach had a high
computational complexity and low PSNR of 40.39 dB.
In [14], Dhargupta et al. proposed a Fuzzy edge detection technique based on steganography and
a modified Gaussian distribution. In the stego-image, the scheme offered a changeable payload.
However, mathematical interpretation was hard, and it was not stable against steganalysis, and
PSNR for payload was low.
Tseng and Leng suggested a high-payload block-based data concealing approach with minimum
distortion utilizing a hybrid edge detector [15]. The algorithm was built on a block-based fuzzy
logic approach. The block-based architecture chooses the right amount of non-edge/edge LSBs
for each block to produce minimal distortion. In terms of payload, the approach has a poor PSNR.
A Steganography approach based on the LoG edge detector was proposed by Ghosal et al. in
[16]. To get a greater payload, a unique embedding technique was applied. This approach has the
advantage of achieving a higher payload. However, the method's biggest downside was its
increased processing complexity.

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Nisreen and Enas used different pixel categories obtained from the three details sub-bands of
Integer Wavelet Transform (IWT) of the cover image starting from the highest category to hide a
secret message in the image's edges in a paper [17]. The approach had a smaller payload capacity.
3. RESEARCH METHOD
In this proposed method, the secret text is compressed twice by an Arithmetic coding algorithm
and then the resulting secret bits are hidden inside the cover pixels of the image corresponding to
the pixels of each of the following two methods, then the two methods are combined together.
One of the two methods is used in each experiment: In the first method, the edges of the image
are modified to increase the number of edges, in the second method the lighter-colored pixels
(which are close to the value of the white color) are selected, then the two methods are combined
together to increase security and keep the secret message unrecognized. Hiding in each of the
previous methods is done by using the LSB technique in the last 2-bit (7th
, 8th
). In the end, the
correction approach is used to increase the stego image's imperceptibility, as shown in Fig. 2.
Figure 2. Workflow diagram showing the phases of the proposed method that have been processed.

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3.1. Arithmetic coding
Arithmetic coding is a lossless encoding technique that creates a code that encodes a fraction in
the unit interval [0, 1]. It's a recursive algorithm. The algorithm divides subintervals of the unit
interval [0, 1] on each recursion. This means that instead of utilizing a sequence of bits to denote
a symbol in arithmetic coding, a subinterval of a unit interval [0, 1] is employed [18].
In the previous research Arithmetic coding algorithm is used to compress the secret message, but
in this paper, the secret message was compressed using the Arithmetic coding algorithm twice to
reduce secret bits as much as possible to get better results than in previous research and improve
the stego image quality. In this algorithm, the secret text is compressed twice in succession by
Arithmetic coding. First applying Arithmetic coding, then the resulting bits are grouped every 7
bits, converted into text and we apply Arithmetic coding again on the new text to get the secret
bits that are hidden.
For example:
 Arithmetic coding is applied once and the resulting bits are =1 1 0 0 1 0 0 1 0 1 1 0 1 0 1 0 0 0
0 0 0
 These bits are grouped every 7 bits
1100100 1011010 1000000
 Convert these blocks to decimal, then to ASCII table = 100 90 64 = d Z @
 Apply Arithmetic coding again on d Z @
 Get the secret bits that are hidden.
3.2. Edges detection method (the first method)
The secret message that is hidden on the image's edges has a smaller effect on the image than if it
had been implemented in the direct least vital bit. The hiding inside the edges is one of the
strongest aspects of the image, and it is not visible due to the strength of the colors in specific
places [19].
In this paper, the edges of the image are modified to increase the number of edges, and the
capacity, where OR binary operation is applied on four edge filters of the cover image, as shown
in fig. 3.
Figure 3. Hiding the secret data in the edges of the image.
In fig. 3, we do OR binary operation between the outputs of each edge filter to increase the
capacity, security, and get the largest number of pixels that the data are hidden inside.

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Figure 4. The edge filter of Lena image.
Fig. 4 shows the types of edge filters on the Lena cover image (256×256), the number of edges in
each filter, and the increasing number of edge pixels in the final image.
3.3. Light regions method (the second method)
In this algorithm, we select the regions with the lightest color, then convert these regions to white
colour and the rest of the image to black, this is done by making the value of pixels greater than
160 in white and others in black as shown in fig. 5. The white color pixels (pixel value = 255) are
the pixels corresponding to the cover pixels of the image that are used to hide the secret bits in it.
When using this algorithm, the results of this method were very close to the results of the
modified edges method, but when we hide a large capacity of the secret bits, and the modified
edge method is used, need additional pixels for the edge pixels to be able to hide all the bits, but
the light regions method is better to hide a larger capacity of the secret bits.
Figure 5. (a) Original image (b) New image.

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3.4. Edge method and light regions method (the third method)
In this algorithm, the method of edges and the light regions are combined together to increase
security and keep the secret message unrecognized. The tent chaotic map equation is used after it
has been modified to apply it on edges and light region pixels. Get values between 0 and 1 when
a tent chaotic map is used, then we make Eq. (2) to get two values which are 1 and 2.
We modified the tent chaotic map equation to use it on two-dimensional matrices. Applying Eq.
(2) on the values obtained from Eq. (1) to hide the secret data in a random way. Hiding the
message is done in this algorithm in a random way, where the value 1 represents hiding the
message by using the light regions method and the value 2 represents the hiding by using the
edges method.
The modified tent chaotic map equation. It's calculated as follows:
 
   
 
   
 , 0.5
,
1 ,
, 1
if x i j
u x i j
u x i j otherwise
x i Q j bQ


    . (1)
Where
 x (i, j) ϵ [0, 1]
 i : row number
 j : column number
 b : total number of image columns
 0 ≤ u ≤ 2, Q= ⌊j/b⌋.
Rand_pixels (i, j) = round (x (i, j) +1). (2)
Where x (i, j) equals the values generated by the chaotic map.
3.5. LSB technique
The LSB approach is a quick and easy way to hide secret data in the spatial domain [20]. It
replaces the least significant bits of the cover image with bits from the secret message, resulting
in a stego-image that resembles the cover image.
In this technique, the secret bits are hidden in the last 2-bit of each pixel corresponding to the
edge pixels, the pixels of the light-colored regions, or edge+region pixels of the image depending
on the method used.
3.6. Correction method
The stego image's imperceptibility is improved using a mathematical method. There are some
variations between a pixel of cover and stego pixel after embedding in some cases [21]. This
method is defined as:
If (S (i, j) – O (i, j) > 2k−1
) & (S (i, j) − 2k
>= 0)
New pixel value of stego = S (i, j) − 2k
Else if (S (i, j) – O (i, j) < −2k−1
) & (S (i, j) + 2k
<= 255)
New pixel value of stego = S (i, j) + 2k
Else

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New pixel value of stego = S (i, j)
Where:
S (i, j): pixel value of the stego.
O (i, j): pixel value of the cover.
K: bits number encoded in each pixel.
For example:
Let O (i, j) = (190)10, S (i, j) = (185)10, k=2
S (i, j) – O (i, j) = 185-190 = -5
If (-5 > 2) & (181>=0)
New pixel= 181
Else if (-5< -2) & (189 <=255)
New pixel= 189
Else
New pixel= 185
Note that the condition after (else if) is executed in this example so, 189 is close to 190.
3.7. Data embedding and extraction algorithm
Algorithm of the embedding secret text
1. Read cover image.
2. Apply one method of edge detection method, light regions method or the two methods are
combined together.
3. Converting cover image to binary.
4. Read and compress the secret text with Arithmetic coding.
5. Apply the modified tent map equation and the Rand_pixels equation on the edge and light
regions pixels if the edges and regions method are combined together.
6. Hide secret bits in the last 2-bit in cover image pixels corresponding to edges, regions, or
edge+region pixels.
7. Applying correction method.
8. Get stego image.
Algorithm of extracting secret text
1. Read the stego image.
2. Applying correction method on stego image.
3. Find the edges or light regions.
4. Apply the modified tent map equation and the Rand_pixels equation on the edge and light
regions pixels if the edges and regions method are combined together.
5. Converting stego image to binary.
6. Look for where the concealed bits are hidden.
7. Obtain bits of the secret information.
8. Arithmetic decoding is executed twice.
9. The secret text is achieved.

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4. RESULTS AND DISCUSSION
4.1. Simulation System
The experiments are conducted using Intel(R) Core(TM) i7-5500U CPU @ 2.40GHz 2.40 GHz
processor with 8.00 GB and system type 64-bit operating system, x64-based processor. The
proposed technique is implemented by using Matlab R2013a under Windows 10 Pro. The
proposed method is tested in this research using a dataset of standard test images and USC-SIPI.
4.2. Experimental Results and Discussion
This section shows the results of applying the suggested method to hide secret text in various
images and measuring the accuracy of the generated images using PSNR, Mean Square Error
(MSE), and the Structural Similarity Index Measure (SSIM) metrics. Higher values indicate
better performance for PSNR and SSIM, while the lower is better for MES. To calculate the
PSNR value, we must first compute the MSE value. The MSE of any stego image can be
calculated using Eq. (3). Any stego image's PSNR can be calculated using Eq. (4). In Eq. (3), M
and N denote the rows and columns of the image, respectively [22].
   
 
2
1 1
1
, ,
M N
i j
MSE I i j S i j
MN  
 
 . (3)
 
2
10
255
10log
PSNR
MSE
 . (4)
The amount of bits embedded in the cover image is known as the embedding payload (capacity)
and it is measured using bits per pixel (bpp).
Capacity = (total number of secret bits) / (M×N). (5)
Where:
I (i, j): cover image's pixel location.
S (i, j): stego image's pixel location.
SSIM is a statistic for comparing two images structural similarity [23]. It's calculated as follows:
 
  
  
2
2 2 2 2
1 2
2 2
, I S IS
I S I S
C C
SSIM I S
C C
  
   
 

   
. (6)
Where:
 , are the average value of the intensity of I and S images (original and stego).
 is variance of I, is variance of S and is the covariance of I and S.
 The two stabilizing parameters are and
 L: pixel's dynamic range ( -1).
 To stabilize the division with a weak denominator, use two variables , .
 = 0.03.

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Table 1. PSNR and SSIM values
Cover PSNR SSIM
Edge Light
region
Edge
+region
Edge Light
region
Edge
+region
Proposed
method
Lena 69.438 69.510 69.387 0.9999 0.9999 0.9999
Boat 69.600 69.701 69.542 0.9999 0.9999 0.9999
Goldhill 69.531 69.533 69.421 0.9999 0.9999 0.9999
[9]
Lena 55.197 0.9983
Boat 55.274 0.9985
Goldhill 55.251 0.9989
In Table 1, images of Lena, Boat, and Goldhill are used as cover images, with dimensions 512 ×
512, and the size of the secret text equal to 14700 bits. The PSNR and SSIM values in this
suggested approach are higher than the approach used in [9] because the hidden message is
compressed twice, hidden in better pixels in the cover image, and the correction method is used.
In [9] a pseudo-random number was utilized in each row to calculate the location of the columns
required to hide secret text without using any other algorithms to improve the result, so the PSNR
value is lower than our method.
Table 2. PSNR, MSE, and SSIM values
Cover PSNR MSE SSIM
Edge Light
region
Edge
+
region
Edge Light
region
Edge
+
region
Edge Light
region
Edge
+
region
Proposed
method
Lena 66.16 66.20 65.945 0.0157 0.0157 0.0165 0.9999 0.9999 0.9999
Peppers 66.04 66.06 66.201 0.0162 0.0162 0.0156 0.9999 0.9999 0.9999
Cameraman 66.01 66.05 66.152 0.0166 0.0163 0.0158 0.9999 0.9999 0.9999
[10]
Lena 63.36 0.030 0.9999
Peppers 63.24 0.031 0.9999
Cameraman 63.22 0.031 0.9999
In Table 2, images of Lena, peppers, and the Cameraman are used as cover images, with
dimensions 256 × 256, and the size of the secret text equal to 1024-byte. The results of the
method used in [10] were 63.3 and the average of our results is 66.1 because [10] hid messages
on the constricted edge areas without using any other algorithms to improve the imperceptibility.

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Figure 6. PSNR values belong to the second table.
Table 3. PSNR results
Method Image capacity PSNR
Light
region
Edge +
regions
Proposed
method
Lena
1.378 57.64 57.65
[11] 1.45 48.32
[12] 1.102 48.08
[14] 1.39 41.40
[15] 1.66 41.03
[17] 0.5 50.96
[17] 0.699 46.04
[21] 0.146 63.48
Proposed
method
Baboon
1.6 55.91 55.93
[11] 1.2 49.61
[12] 1.208 46.58
[14] 1.99 39.10
[15] 1.80 40.22
[21] 0.412 58.37
Proposed
method
Airplane
3.0 51.34 51.39
[11] 1.27 49.20
[12] 1.108 47.82
[14] 1.49 41.29
[15] 1.65 41.02
Proposed
method
Pepper
2.01 53.74 53.81
[11] 1.33 48.91
[12] 1.094 48.24
[14] 1.47 41.38
[21] 0.137 63.23

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In Table 3, Lena, Baboon, and Airplane images are utilized as a cover grayscale image with
dimensions 128 × 128, and the secret bits are hidden in the last 4 bits of each pixel used. In [11]
the largest number of edges was 4562, while using the light regions method, the number of pixels
that are hidden inside became 5680 pixels, and the value of PSNR is better due to the
compression of secret bits and other algorithms used. Compared with other research, the value of
PSNR is better, with more capacity hidden, because we compress the secret data twice, and the
number of bits decreases. Also, the correction method reduces the difference between the cover
image and the stego image.
When hidden within an Airplane image, the maximum number of bpp in our proposed method is
3.0 bpp, although the PSNR value is better than other comparisons. When using the Lena image a
maximum payload capacity was 0.699 bpp and PSNR of 46.04 in [17], unlike our method has a
maximum payload equal 1.378 bpp.
Table 4. Comparison the number of edges or pixels used to hide secret data using different methods and
cameraman image as grayscale image with dimensions 128 × 128.
Figure 7. The number of edge pixels belong to the fourth table.
Table 1, 2, and 3 show that the PSNR values for the suggested approach are greater than those for
the other approaches.
Method Number of edges (pixels)
Canny 1471 pixel
Sobel 857 pixel
Prewitt 1115 pixel
Hard Thresholding in [11] 2506 pixel
Otsu Thresholding in [11] 3329 pixel
Mean Adaptive Thresholding
in [11]
4078 pixel
Gaussian Adaptive
Thresholding in [11]
4562 pixel
Proposed method (light
region)
5680 pixel
Proposed method (modified
edges)
2298 pixel

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For example, to hide 50,000 bits in Lena cover image with dimensions 512×512 (we hide secret
bits in last 2-bit need 25,000 pixel) and use the following different methods:
 Simple Canny edge: we can hide bits in only 6241 pixel.
 Simple Prewitt edge: we can hide bits in only 2576 pixel.
 Modified edges in this research: we can hide bits in 9684 pixel.
 Light regions method in this research: we can hide secret bits in 60155 pixel.
 Edge + region method in this research: a method that relies on randomly selecting pixels as
well as masking a high capacity.
 Total number of modified edge pixels in [10] = 14487 pixel.
Figure 8. The number of edge pixels for Lena image with dimensions 512×512.
Table 5. PSNR values
Method Message
size
Cover PSNR
Light regions
Proposed
method 1024-byte
Lena 63.368
Pepper 63.244
[10] Lena 63.364
Peppers 63.242
Light regions
Proposed
method 512-byte
Lena 66.301
Peppers 66.376
[10] Lena 66.388
Peppers 66.283
Table 5 shows that when using the light regions method without secret data compression and no
correction method is used, the PSNR value is equal to the same value when using edge method in
the paper [10]. Hiding inside light areas has the same effect as hiding using edges, but it's better
because it allows us to hide a large amount of secret data. So, the Edge + region method is the
best because it made the secret message more secure and enabled us to hide a greater capacity of
the secret data. The message compression is used twice, and the discrepancy between the cover
image and the stego image is reduced using the correction method, all of which led to better
results.

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Image histogram
An image histogram is a type of histogram that uses a graphical representation of a digital image
of the tonal distribution. Histograms of photographs are seen on the majority of new digital
cameras. Photographers can use it to see how colors are dispersed in the image and whether the
brightness has been lost owing to blown-out lights or blacked-out shadow. The horizontal axis of
the graph defines tonal changes, whereas the vertical axis determines the overall pixel count in
that given tone [24].
Figures 9, 10 show the histogram of Lena and Baboon image when secret text equals 4096 bytes
and how similar the histograms of the cover and stego image are.
(a) Edge method (b) Light region method
(c) Edge+ Light region method
Figure 9. Histogram of Lena image.

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(a) Edge method (b) Light region method
(c) Edge+ Light region method
Figure 10. Histogram of Baboon image.
5. CONCLUSION
Three methods have been proposed in this paper to hide secret text by them are the modified
edges method, the light regions method, and the edges + light regions method. The light regions
method is better than the modified edges method because it has a larger number of pixels and a
greater number of bits are hidden when used. The Edges + light regions method is better than the
other two methods because the process of hiding the secret bits was done randomly, which leads
to increasing robustness, security, and maintaining the secrecy of the secret text. The secret text is
compressed twice to reduce the number of bits that are hidden. The correction approach is used to
increase imperceptibility by reducing the disparity between the cover image and the stego image.
PSNR, MSE, SSIM, histogram measurements, and comparisons with other previous procedures
employing different methods were all part of the test. According to experimental data, the
proposed solution improves the quality of the stego image by increasing imperceptibility,
embedding capacity, and robustness.
According to the experimental results, get a higher average PSNR value for the second proposed
(Light regions) method equals 62.76 dB, and for the third proposed (Edge and region) method
equals 62.72 dB, and many pixels are similar in effect to edge pixels, which carries a greater
capacity for secret bits. When using a Lena image with a size of 512 × 512 and hiding by the
light regions method, the number of pixels increased by 17.4% compared with the method used in
other research and at a rate of 19.3% than the modified edge method used in this research.

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In future work, to improve the PSNR values, develop a mechanism for selecting hidden bits that
are similar to the secret bits on RGB images.
ACKNOWLEDGEMENTS
The authors are grateful to the confidential referee for carefully reviewing the original manuscript
and providing helpful comments that improved the presentation of the data and highlighted
critical details.
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AUTHORS
Mayar khaled. Is currently a demonstrator at mathematics department, computer science
division, Faculty of Science, Benha University, Egypt.
Ahmed H. Abu El-Atta. PhD of computer science, Benha University, Benha, Egypt,
March 2018. Master of computer science, Benha University, Benha, Egypt, March 2011.
Bachelor of computer science, Benha University, Benha, Egypt, May 2005.
Current position Lecturer, Department of computer science, Benha University, Benha,
Egypt, from July 2018 until now.

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