International Journal of Mechanical Engineering and Technology (IJMET)
Volume 10, Issue 01, January 2019, pp. 2066-2074, Article ID: IJMET_10_01_202
Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=1
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
© IAEME Publication
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GENETIC ALGORITHM BASED
STEGANOGRAPHY USING ADAPTIVE
RECTANGULAR EMBEDDING AREA
AHMED KAWTHER HUSSEIN
Department of Computer Science, College of Education,
Mustansiriyah University, Baghdad-Iraq
ABSTRACT
Image steganography is to hide an image inside an image for security purpose.
Different approaches were applied for image steganography some of them are based
on spatial domain and others are based on frequency domain. Spatial domain
steganography approaches are simpler because they do not involve applying any
transformation to the image. In this article, genetic algorithm is presented for
searching for the best locations to hide the image bits inside the image. The image can
be embedded in an adaptive block inside the host image. Furthermore, the secret bits
can be reserved and inverted before embedding in order to optimize the hiding
performance measures. Results show superiority over the baseline approach with
respect to all evaluation measures
Keywords: Stenography, genetic algorithm, meta heuristic searching, image
embedding.
Cite this Article: AHMED KAWTHER HUSSEIN, Genetic Algorithm Based
Steganography using Adaptive Rectangular Embedding Area, International Journal of
Mechanical Engineering and Technology, 10(1), 2019, pp. 2066-2074.
http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=10&IType=1
1. INTRODUCTION
The science of hiding messages in a medium called cover or carrier object is Steganography
like Text, image, audio, video, etc. [13] in a way that presence of the message is not
discovered. Today, like transaction and communication have become on the Internet wellknown, securities of information technology and communication have become essential.
Steganography is playing very important part in securing the digital world communication.
The essential steganography intentions are to assure the necessary of the steganography
system security, defend the confidential data from being increase the embedding capability
and reached by invadors.
Among all steganography techniques, image steganography has an advantage that the
digital images have a right amount of irrelevant data, which can be formed to cover hidden
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AHMED KAWTHER HUSSEIN
information without influencing too much distortion in the cover image. The reason of
preferring image steganography over other types is that images are the most exchanged
medium between people in the virtual world which makes very convenient to hide secret
messages in images. Various steganography techniques hide the information behind image
files, one of them is Spatial Domain Techniques, and this technique encodes the message by
using pixel gray level along with their color values. Three types of embedding approaches for
steganography were followed: the first type uses the frequency domain for embedding.
Examples are found in [6] [10], [20], [22], [1] while the second type uses the spatial domain,
examples are found in [17], [3].In the third type, combination of them. Some researchers have
combined the spatial and frequency domains. The spatial domain aims at inserting the secret
bits in the lower weights bits [19],[18],[3],[23],[14],[16]. Example is to use the least
significant bit of the cover image to hide a whole message. The limitations of the spatial
embedding techniques are their vulnerabilities to image-processing operations, noise attacks;
lossy compression, filtering and they are subject to statistical attacks. Examples of
steganalysis methods [2], [4], [15]. In the frequency-domain steganography, various domain
transforms are used such as discrete cosine transforms (DCT), discrete wavelet transforms
(DWT), and discrete Fourier transforms (DFT). DWT is preferred because of its robustness
against image-processing operations, statistical and noise attacks as well as distortion [5].
However, both DCT and DWT have smaller capacities. Also, DCT was used by numerous
researchers DCT [6], [10], [9],[8] and DWT [1], [20]. DFT is not preferred because of
rounding error in the inverse transform. The three mentioned type’s techniques are used with
other techniques including artificial neural networks (ANN), meta-heuristic approaches or
both to attain enhanced stenographic performances. Spatial-domain genetic algorithms GAs
are used [14], [15] to decrease the distortion. Genetic algorithm and artificial neural networks
are used [7] to accelerate the training speed. Frequency-domain ANN is used [21] to increase
the embedding capacity. Spatial domain artificial neural networks (ANN) is utilized [12] to
attain a realize good approximation capacity, faster convergence, and a more stable
performance surface. This type of artificial neural networks (ANN) is moreover used [11],
[23] to decrease the distortion and increase the approximation capacity.
2. PPROPOSED METHOD
Our proposed approach is based on some terms and concepts were presented by [15]. We
define them as: Raster order: It represents the order of pixels while scanning the image for
embedding. There are 16 possible directions in the raster order, and they are presented in
Figure 1 More number of possibilities of raster orders is to change the starting point. For 3 3
pixels image, there are 144=9 16 case. Other
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Genetic Algorithm Based Steganography using Adaptive Rectangular Embedding Area
Figure 1 Raster order and different possible orientation of the raster order
[15] have proposed the information in the table 1 to be represented in the solutions:
Table 1 The information of inserting bits inside spatial domain
Information
Roster direction
Starting point
Bit-planes
Bit-planes
direction
Secret poles
Secret bits
direction
Meaning
The direction of host image pixel scanning for
embedding
The starting point of scanning in the host image
The positions from LSB in the host image pixels for
embedding
The direction of embedding in the pixels of the host
image
The bits of secret are embedded as they are or after
inverse
The direction of inserting the secret bits
In [15] authors proposed a method for hiding message bits inside an image called host
image using Genetic Algorithms GA. Hiding a message in an image could be done in
different places where each place causes different distortion in the image. GA has been used
to determine the best place for hiding data in the host image. The best place means the place
that the message could be hid inside with least possible distortion. This method determines
the start pixel, number of used least significant bits in each pixel, and the sequence of
scanning image pixels in order to hide message bits. Because the mentioned paper uses
sequential method in scanning image pixels (columns from right to left or reverse) and (rows
from up to down or reverse) in the whole image, it puts a constraint in finding the best place
to hide the message where it could be better to scan inside a window from the image instead
of scanning inside the whole image. In order to enhance the ability of the mentioned method,
the sequential scanning inside whole image has been replaced to sequential scanning inside a
window from the image with suitable size for the message. In this manner, the old way is a
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particular case from the proposed method and the new method is a generalization of the old
method.
In the proposed way, the solution has the following variables:
Where
represents the offset of the embedding sub-area with respect to x axis
represents the offset of the embedding sub-area with respect to y axis
represents the direction of the embedding sub-area in the host image
represents the positions of the embedding bits in the host image
represents the bit poles in the secret image
represents the bit direction in the secret image
represents the width of the embedding sub-area with respect to x axis
represents the width of the embedding sub-area with respect to y axis
It is important to note that this optimization follows the following constraints
the dimension of window in the x-axis
the dimension of window in the y-axis
Also, there are two constraints must be satisfied:
+ <= number of host columns
+ < number of host rows (Strict inequality because the last line is used for hiding
chromosome bites)
The particular bits are embedded in the last row of message in order to extract the
information in the destination.
The objective function that has been used is the PSNR between the host image and stegoimge. The default genetic toolbox in MATLAB was used for the purpose of performing the
optimization.
3. EXPERIMENYAL RESUULTS
This section presents the experimental work for evaluating the developed approach. We
selected six images from different domains; they are shown in the figure-2-. We ran the
optimization algorithm on the six images; it converged to the best area to embed the image
inside the host image. Figure-3- shows the results of embedding, the red box is the sub-area
where the secret image was embedded inside the host image.
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Genetic Algorithm Based Steganography using Adaptive Rectangular Embedding Area
Figure 2 The host images for testing our developed stego approach
Figure 3 Images after Embedding The Secret Image With Showing The Red Box Where The Secret
Image Was Embedded
Four evaluation measures were generated for each image and compared for the host
image and the stego image. Figure 4 show PSNR between host and stego images for both our
approach and the baseline approach for the six images. Obviously, our developed approach
has achieved higher PSNR which indicates to better quality of embedding. The same is
applied for the SSI shown in Figures 5 and correlation measures shown in Figures-7-. Also,
Figure 6 show MSE between host and stego images for both our approach and the baseline
approach for the six images. Obviously, our developed approach has achieved lower MSE
which indicates to better quality of embedding.
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Figure 4 PSNR Between Host and Stego Image for The Tested Images
Figure 5 SSI Between Host and Stego Image for The Tested Images
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Figure 6 MSE Between Host and Stego Image for The Tested Images
Figure 7 Correlation between Host and Stego image for The Tested Images
4. COMPARISON BETWEEN THE BASELINE AND OUR APPROACH
The essential difference between our approach and the baseline approach is the more degree
of freedom that is attained in our approach when embedding the secret bits inside the host
image. In our approach we do not consider that the secret bits should be hidden in sequential
manner, rather, it assumes that any block with any height and width can be applied to embed
the bits of secret. As a result, our approach avoided local minima in the searching and
provided better results than the baseline approach.
5. CONCLUSION
In this article, the article has relied on the original work of [14] that uses genetic algorithm for
modeling the steganography problem. In this article, a generalization of the optimization
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approach developed by [14] that allows embedding the image in any sub-area inside the host
image with adaptive height and width of the embedding area in order to maintain best
embedding measures. Results have shown a superiority of our developed approach over the
baseline approach with respect to all evaluation measures: PSNR, SSI, MSE, and correlation.
Future work is to more develop the approach to increase the freedom degree of the embedding
process.
ACKNOWLEDGMENTS
The author would like to thank the Mustansiriyah university (www.uomustansiriyah.edu.iq)
Baghdad - Iraq for its support in the present work.
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