An Efficient Neural Network based Algorithm of Steganography for ...

ISSN 2249-6343
International Journal of Computer Technology and Electronics Engineering (IJCTEE)
Volume 1 , Issue 2
An Efficient Neural Network based Algorithm of
Steganography for image
Imran Khan
Abstract—To provide large capacity of the hidden secret data
and to maintain a good visual quality of stego-image a novel
steganographic method based on neural network and random
selection of edged areas of pixels is proposed in this research
paper. Firstly a cover image is divided into a non-overlapping
two pixels block and this pixel block generates a set of edged
non-overlapping regions. After this a neural network is applied
which generates a stego-image which has been immune against
conventional attack and performs good perceptibility compared
to other steganographic approaches. From our experimental
results it can be shown that the proposed method hides
information in edged regions and maintains a better visual
display of stego image than the traditional methods.
Index Terms: Coverimage, LSB, Stego-image, and
Steganography.
I. INTRODUCTION
With advancements in digital communication technology
and the growth of computer power and storage, the difficulties
in ensuring individual’s privacy become increasingly
challenging. The degrees to which individuals appreciate
privacy differ from one person to another. Various methods
have been investigated and developed to protect personal
privacy. Encryption is probably the most obvious one, and
then comes steganography. Encryption lends itself to noise
and is generally observed while steganography is not
observable. Steganographic techniques [1] have been used for
centuries and being applied across a broad set of different
digital technologies. It has been observed that terrorists have
been utilizing steganographic techniques within digital
images to communicate to the internet [2].
A. Implementing steganography
Secrets can be hidden inside all sorts of cover information:
text, images, audio, video and more. However, there are tools
available to store secrets inside almost any type of cover
source. A possible way of storing a secret inside a text is using
a publicly available cover source, a book or a newspaper, and
using a code which consists for example of a combination of a
page number, a line number and a character number [2].
Hiding information inside images is a popular technique
nowadays. An image with a secret message inside can easily
be spread over the World Wide Web or in the newsgroups.
The use of steganography in newsgroups has been researched
by German steganographic expert, who created a ‘scanning
cluster’ which detects the presence of hidden messages
inside images that were posted on the internet.
To hide a message inside an image without changing its
visible properties, the cover source can be altered in ”noisy”
areas with many color variations, so less attention will be
drawn to the modifications. The most common methods to
make these alterations involve the usage of the
least-significant bit or LSB, masking, filtering and
transformations on the cover image. The LSB method [3] uses
bits of each pixel in the image. When using a 24 bit color
image, a bit of each of the red, green and blue color
components can be used, so a total of 3 bits can be stored in
each pixel.
Masking and filtering techniques usually restricted to 24
bits or grayscale images. These methods are effectively
similar to ‘paper watermarks’, creating markings in an image.
This can be achieved for example by modifying the luminance
of parts of the image. While masking does change the visible
properties of an image, it can be done in such a way that the
human eye will not notice the anomalies. Least Significant Bit
maintains a good visual quality of stego-image, it can hide
little information. Considering the drawback of LSB, some
methods begin to take account of the visual identity that
human eyes are insensitive to edged and textured areas
when embedding secret information, such as BPCS(biplane
complexity segmentation)[4],PVD(pixel value
differencing[5]), MBNS (multiple base notational system
[6]), SOC, Side Match [7] and WCL.The capacity of
embedded information is thereby greatly improved while the
quality of visual imperceptibility is maintained. As human
vision sensitivity is complex, it is hard to exactly decide
whether a pixel is in less sensitivity areas or not. Thus,
based on the contrast and texture sensitivity, we train
self-organizing map Neural Networks (NNs) trained to
distinguish pixels in less sensitive areas from pixels in
more sensitive areas. So, NNs trained is the secret key.
Then, we use NNs trained to classify pixels, and select
pixels in less sensitive areas to embed more secret data. On
the receiving side, the original image is not needed
for extracting the embedded data.
Basic elements of steganography in images are shown in
Figure 1. The carrier image in steganography is called the
"cover image" and the image which has the embedded
data is called the "stego image". The embedding process is
usually controlled using a secret key shared between the
communicating parties.
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ISSN 2249-6343
International Journal of Computer Technology and Electronics Engineering (IJCTEE)
Volume 1 , Issue 2
This key ensures that only recipients who know the
corresponding key will be able to extract the message from a
stego image.
Sabeti et. al [14] have successfully attacked basic PVD and
the enhanced version of PVD.
Table 1: Example of an image
11 15 19
12 13 18
14 16 17
Table 2: Vectorized image
11 15 19 …. … 17
Fig 1. Typical element of any steganographic system
II. PIXEL VALUE DIFFERENCING METHOD
A popular digital steganography technique is the so-called
least significant bit (LSB) replacement. With the LSB
replacement technique, the two parties in communication
share a private secret key that key that creates a random
sequence of samples of a digital signal. The encrypted
secret message is embedded in the LSB's of those samples
of the sequence. This digital steganography technique takes
the advantage of random noise present in the acquired images
[8]. Many reliable steganalytic methods have been devised
for LSB flipping technique. The production of Pair of
Value (PoV) in the histogram of a stego image is the
main weak point of the LSB flipping. The presence of
PoV has allowed many steganalysis methods, such as RS to
successfully attack LSB flipping[10,11] .
Wu and Tsai [12] presented a steganographic
method based on Pixel Value Differencing (PVD). They
divide the cover image into a number of non- overlapping
two-pixel blocks. Each block is categorized according to the
difference of the gray values of the two pixels in the block. A
small difference value indicates that the block is in a
smooth area and a large one indicates that it is in an
edged area. The pixels in edged areas may tolerate larger
changes of pixel values than those in the smooth areas.
Therefore, it is possible to embed more data in edged areas
than in the smooth areas. All possible difference values
are classified into a number of ranges. The number of bits
that are to be embedded in a pixel pair depends on the
width of the range that the difference value belongs to.
PVD is immune against the attacks that scrutinize changes in
spatial domain [11] or changes in the histogram [10].
In [13] another method based on PVD is proposed which
tries to increase the embedding capacity of PVD. In this
version the image blocks are categorized based on the
calculated pixel differences into two groups of smooth and
edge. A block with difference less than a threshold is a
smooth block; otherwise, it is an edge block. LSB embedding
is used for smooth regions and PVD embedding for edged
areas. We refer to this method as the enhanced PVD approach
as opposed to the basic PVD approach discussed in [10].
The cover images used in the PVD method are supposed to
be 256 gray-valued ones. A difference value d is computed
from every non-overlapping block of two consecutive pixels,
say p i and p i+1 of a given cover image. Partitioning the
cover image into two-pixel blocks runs through all the
rows of each image in a zigzag manner. In Figure 2, an
example of an image is shown. The two-pixel blocks that are
constructed by zigzag scanning of the example image are
shown in Figure 3.
Assume that the gray values of p i and p i+1 are g i and g i+1 ,
respectively; then d is computed as g i+1 -g i which may be a
number ranging from -255 to 255.A block with d close to 0 is
considered to be an extremely smooth block, where as a block
with d close to -255 or 255 is considered as a sharply edged
block. The method only considers the absolute values of d (0
through 255) and classifies them into number of contiguous
ranges, such as R k where k = 1, 2, …, q by l k and u k ,
respectively, where l 1 is 0 and u q is 255.
The width of R k (w k ) is u k - l k +1. In PVD method, the width
of each interval is taken to be a power of 2.A practical set of
intervals may be: [0; 7]; [8; 15]; [16; 31]; [32; 63]; [64;
127]; [128; 255].Every bit in the bit stream should be
embedded into the two-pixel blocks of the cover image. Given
a two-pixel block B with gray value difference d belonging to
k th range, then n bits can be embedded in this block, and can
be calculated by n = log(u k + l k + 1) which is an integer.
A sub-stream S with n bits is selected next from the secret
message for embedding in B. A new difference d’ then is
computed by:
Here b is the value of the sub-stream S. Because the value b
is in the range from 0 to u k - l k , the value of d’ is in the range
from l k to u k. If we replaced d with d’, the resulting changes
are presumably unnoticeable to the observer. Then b can be
embedded into pixels p i and p i+1 in a manner that the new pixel
values produce a difference of d0.
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ISSN 2249-6343
International Journal of Computer Technology and Electronics Engineering (IJCTEE)
Volume 1 , Issue 2
The new gray values (g’ i , g’ i+1 ) are obtained for the pixels
in the to the corresponding two-pixel block (p’ I ,p’ i+1 ) of the
stego-image. The embedding process is finished when all the
bits of the secret message are embedded. The calculation for
computing (g’ i , g’ i+1 ) from the original gray values (g i , g i+1 ) of
the pixel pair is based on a function f((g i , g i+1 ),m) which is
defined to be
Once network weights are converged to certain values,
proposed method use these values and the coordinates of
selected feature sub blocks as extraction keys. This extraction
keys must be shared among the embedder and the extractor in
order to extract proper hidden signals from the contents. First,
frequency transformation of the image is performed. Same
amount of unique sub blocks as number of classification
patterns must be chosen from the cover image. Sufficient
number of neural networks must be prepared, which will be
the number of binary digits to satisfy the classification
patterns.
Where m is d’- d. obviously embedding is only considered
for pixels whose new pixel values would fall in the range of
[0,255].
III. PROPOSED METHOD
In this section, we explain how proposed method hides
information in stego-image and how we retrieve information
from the stego-image. With our method, the use of neural
network is the key technique. Firstly a cover image is divided
into a non-overlapping two pixels block and these pixel block
generates a set of edged non-overlapping regions .All these
blocks are generated by using PVD differencing
method.Now,the embedder adjusts a neural network weights
with desired hidden bit code from the collection of both cover
image and stego-image folder(i.e. our training data). This
pattern is common for both embedding and extracting the
hidden information. For learning we can use supervised
learning of the neural network. By using XOR neural network
learning model we embed the secret message within a cover
image. At the first pixel location of stego image we embedded
the message length and the edged pixel maps of a cover image
A. Information hiding algorithm
In the information hiding algorithm firstly we generated a
set of those pixels which are highly suited for
steganography.These pixels are generated by taking the pixel
difference of two adjacent pixel block .This technique is
described in section 2.Now in training, one must decide the
structure of neural network. The amount of units for input
layer is decided by the number of pixels selected from set of
edged region pixel map. In proposed method, the feature
values are diagonal coefficient values from frequency
transformed selected feature sub blocks. For better
approximation, one bias neuron is added for input layer. The
neural network is trained to output a value of 1 or 0 as an
output signal. In proposed method, one network represents
one binary digit for corresponding secret codes.
The adequate amount of neurons in the hidden layer, for
back propagation learning in general, is not known. So the
number of neurons in hidden layer will be taken at will. In
proposed method, six hidden units are used. For better
approximation, one bias neuron like is introduced for hidden
layer as well.
Fig 2. Flow Chart of Proposed Method
In case for 32 input patterns, five networks are enough to
represent 32 different identification values because five
binary digits are sufficient to distinguish for 32 patterns.
Learning of all networks is repeated until the output value
satisfies a certain learning threshold value. After all network
weights are converged, the coordinates of sub blocks and the
values of network weights are saved. Extractor will use this
information to extract hidden codes in the extraction process.
B. Extraction algorithm
Extraction process starts from the reading of stego-image
and a extraction key information. After providing this
information we calculate the total length of the message which
will embed with a stego-image. We also retrieve the
information of edged region pixel set from stego-image. Only
by knowing the proper coordinates of feature sub blocks will
lead user to the proper input values. By knowing proper
network weights, extractor can induce the structure of the
network and only proper network weights are able to output
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ISSN 2249-6343
International Journal of Computer Technology and Electronics Engineering (IJCTEE)
Volume 1 , Issue 2
the proper hidden codes. After constructing the neural
network, extractor examines the output value from the
network with the input values induced from the feature sub
blocks. This procedure is shown in Figure 4. Each network
output either 1 or 0 with the aid of threshold for output unit.
The flow chart for calculation of extraction is given as
Fig 3. Flow Chart for Calculation of extraction algorithm
IV. IMPLEMENTATION AND MEASURES
Dot net platform is chosen to develop the above
steganographic algorithm. In a Dot net framework there are
extensive libraries and efficient functions of neural network
and image processing which is very useful in steganography.
Developers may use other programming language
also.Security, embedding distortion and embedding rate can
be used as schemes to evaluate the performance of the data
hiding schemes.
A. Entropy
A steganographic system is perfectly secure when the
statistics of the cover data and the stego data are identical,
which means that the relative entropy between the cover data
and the stego-data is zero. Entropy considers the information
to be modeled as a probabilistic process that can be measured
in a manner that agrees with intuition [14].The information
theory approach to steganography holds the systems’ capacity
to be modeled as the ability to transfer information. More
information regarding information theory and its application
to steganography can be found at [14].
B. Mean Squared Error & SNR
The (weighted) mean squared error between the cover
image and the stego-image (embedding distortion) can be
used as one of the measures to assess the relative
perceptibility of the embedded text. Imperceptibility takes
advantage of human psycho visual redundancy, which is very
difficult to quantify. Mean square error (MSE) and Peak
Signal to Noise Ratio (PSNR) can also be used as metrics to
measure the degree of imperceptibility.
MSE= [ M i=1 N j=1 ( f ij – g ij ) 2 ] MN
PSNR=10 log 10 (L 2 / MSE)
f ij is the pixel value from the cover image , g ij is the pixel
value from the stego-image, and L is the peak signal value of
the cover image (for 8-bit images, L=255). Signal to noise
ratio quantifies the imperceptibility, by regarding the message
as the signal and the message as the noise. Thus, the higher the
SNR, the more perceptible is the message.
SNR = 2 S / 2 N
C. Correlation
Correlation is one of the best known methods that evaluate
the degree of closeness between two functions. This measure
can be used to determine the extent to which the original
image and the stego-image are close to each other, even after
embedding data Localization, that is detection of the presence
of the hidden data relies on the use of cross correlation
function R XY of two images X and Y, defined as[ 8],
R XY ( , ) = i j X(i,y) Y(i-,j-)
D. Ensuring Integrity-using Checksums
In order to ensure the integrity of data and the cover
medium, mechanisms should be employed that either detect
that the medium has been altered or is able to withstand such
changes and corrects them to the original state. Checksums
could be used to alert the user of possible contamination or
tampering. For monochrome images the application of
checksums is going to straightforward with the checksums
being calculated for the appropriate number of bits required to
represent each of the pixels. For color images, the checksum
scheme can be extended three times to the three-color planes.
The checksum could also be calculated in a new coordinate
system, for e.g., hue saturation intensity plane instead of RGB
plane, and the resulting checksum could be embedded in the
original coordinate plane.
V. RESULT ANALYSIS
In this experiment we used PNG and BMP images of
various resolutions as cover image. We train a set of 150
images which is randomly taken from internet. These images
have various memory sizes.
Table 3: Dataset Taken for consideration
proposed method LSB Steganography
COVER IMAGE PSNR PSNR
Federar.bmp 39.93 29.67
Beach.bmp 43.907 33.198
lily.bmp 37.86 34.26
LENA.BMP 30.16 28.95
PEPPERS.PNG 47.88 38.7
FLOWER.PNG 36.16 32.65
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ISSN 2249-6343
International Journal of Computer Technology and Electronics Engineering (IJCTEE)
Volume 1 , Issue 2
By performing the comparison of the cover image and the
stego-image of above two methods datasets we come to
conclude that the perceptibility ratio of the proposed method
is better than the LSB steganography technique.
Fig.4.Performance analysis in terms of PSNR ratio
VI. CONCLUSION AND FUTURE WORK
Given the high degree of redundancy present in a digital
representation of image, there has been an increased interest
in using it for the purpose of steganography. The paper
suggested how a selection of different portions of the images
increased the chances of hiding data in a more effective way.
This type of embedding also reduces the chances of detection
of secret message. Variation of the LSB [8] insertion
algorithm. This algorithm can be used for achieving better
security and also improved covertness. However for the future
work of this method, we recommend the secret message
should be compressed or encoded before the hiding process
takes place. This is important because in this way we will
minimize the amount of information that is sent, and hence
minimizing the chance of degrading the image. At the same
time results of steganalysis can be used to change or improve
embedding techniques.
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AUTHOR BIOGRAPHY
Imran Khan received his Bachelor’s Degree in
Computer Science & Engineering from Rajiv Gandhi
Proudyogiki Vishwavidyalaya, Bhopal (M.P.)-India
in year 2005 and M. Tech. (Information Technology)
from Rajiv Gandhi Proudyogiki Vishwavidyalaya,
Bhopal (M.P.)-India in year 2011. He is an Assistant
Professor in Oriental Institute of Science and
Technology, Bhopal in the Department of
Information Technology. His research areas are
neural network, artificial intelligence and network
security. He is having six research papers in International and national
Journals
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