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- 1. ISSN: 2277 – 9043
International Journal of Advanced Research in Computer Science and Electronics Engineering
Volume 1, Issue 4, June 2012
Biogeography Based Steganography for Color
Images
Er.Rishma Er.Lakhvir Singh Er.Krishma Bhuchar
Assistant Professor Research Scholar Assistant professor
RIET RIET KCCEIT
Phagwara, Punjab. Phagwara, Punjab. Nawanshahr, Punjab.
Abstract:- Steganography is an art that involves watermarking as shown in figure 2. In the first application,
communication of secret data in an appropriate carrier, e.g., a digital image contains a secret message. The advantage
image, audio, video or TCP/IP header file. Steganography’s of steganography is that those who are outside the party
goal is to hide the existence of embedded data so as not to even do not realize that some sort of communication is
arouse an eavesdropper’s suspicion. For hiding secret data in
digital images, large varieties of steganographic techniques
being done [2].
are available, some are more complex than others, and all of
them have their respective pros and cons. This paper intends
to give thorough understanding and evolution of
biogeography based optimization technique for data hiding.
It covers and integrates recent research work without going
in to much detail of steganalysis, which is the art and science
of defeating steganography. In addition, our proposed
method computes performance evaluation in terms of
computational time of 21.2 seconds as compared to other
evolutionary algorithm. It has good optimization
performance due to its migration operator. Therefore,
Biogeography Based technique is more reliable and faster for
Secret Image Cover Image Stego Image
Image Steganography.
Index Terms: - Biogeography, Image segmentation, RGB FIGURE. 1: The block diagram of a simple
(Red, Green and Blue) model, Steganography, Computational
time. steganographic system
The main applications of such a scheme are to transmit
I. INTRODUCTION secret data. In the second application, a short message (a
To understand the implementation of Image watermark) is embedded in the image in a robust manner.
Steganography using Biogeography Based Optimization, Many robust techniques including statistical methods,
Firstly have to understand some terms that are discussed signal transformation, the spread spectrum method,
given below: Discrete Cosine Transform (DCT), Discrete Fourier
Transformation (DFT), a wavelets-based technique,
1.1. Steganography Fourier–Mellin transformation, fractal-based methods and
Information hiding is an old but interesting technology. a content-based method can be efficiently applied to
Steganography is a branch of information hiding in which watermark digital images [3]. The stego-images generated
secret information is camouflaged within other by these methods can survive common image processing
information. The word steganography in Greek means operations, such as lossy compression, filtering, the adding
―covered writing‖ (Greek words ―stegos‖ meaning ―cover‖ of noise, geometrical transformation, and others.
and ―grafia‖ meaning ―writing‖) [1]. The main objective of
steganography is to communicate securely in such a way
that the true message is not visible to the observer. That is
unwanted parties should not be able to distinguish any
sense between cover-image (image not containing any
secret message) and stego-image (modified cover-image
that containing secret message). Thus the stego-image
should not deviate much from original cover-image.
figure.1 shows the block diagram of a simple image
steganographic system. Depending on the form of type of
information hidden in digital images, data hiding schemes
can be roughly divided into two major categories––non-
robust, undetectable data hiding, and robust image Figure 2:-Steganography
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International Journal of Advanced Research in Computer Science and Electronics Engineering
Volume 1, Issue 4, June 2012
1.1. 1 Applications of Steganography that an individual receives a feature from the rest of the
Steganography can be used for wide range of applications population is decreases with its fitness [5].
such as, in defence organisations for safe circulation of The values of emigration and immigration rates are given
secret data, in military and intelligence agencies, in smart λ = I (1-K/n)
identity cards where personal details are embedded in the µ=E/n
photograph itself for copyright control of materials [3]. In Where I is the maximum possible immigration rate; E is
medical imaging, patient’s details are embedded within the maximum possible emigration rate; k is the number of
image providing protection of information and reducing species of the k-th individual; n is the maximum number of
transmission time and cost1, in online voting system so as species.
to make the online election secure and robust against a Emigrating Islands
variety of fraudulent behaviours2, for data hiding in
countries where cryptography is prohibited, in improving
mobile banking security3, in tamper proofing so as to
prevent or detect unauthorized modifications and other
numerous applications [8] as shown in figure3.
Steganogarphy Applications
Copy Protection Authentication
Immigration Islands
Documents secret
Figure 4:- Biogeography Based Optimization
Annotation concealed
Communication
= the probability that the immigrating individual’s
Medical Images, Military solution feature is replaced.
Multimedia databases = the probability that an emigrating individual’s
solution feature migrates to the immigrating individual.
Figure.3. Applications of steganography
BBO basically depends upon following theory:-
1.2. Biogeography-Based Optimization a) Migration
Biogeography Based Optimization (BBO) is a recently The BBO migration strategy in which many parents can
developed heuristic algorithm which has shown impressive contribute to a single offspring, but it differs in at least one
performance on many well known benchmarks. important aspect. BBO migration is used to change
Biogeography Based Optimization is based on the existing habitat [6].
mathematical study of biogeography as shown in figure For i= 1 to NP do
4[5]. Each island has its characteristics such as food Select Ii with probability based on λ i
availability, rainfall, temperature, diversity of species, If Ii is selected then
security, population of species etc. The quality of an island For j=1 to NP do
is measured by its suitability index (SI). Islands with HSI Select Ij with probability based on μ j
are more suitable for living and therefore have large If Ij is selected
population while those with LSI have sparse population Randomly select a SIV v from I j
due to the fact that of suitability or friendly for living. HSI Replace a random SIV in Ii with v
islands have low immigration rate λ and high emigration End if
rate μ simply due to high population.HSI has less dynamic. End for
By the same virtue, islands with LSI have high b) Mutation
immigration rate λ ¸ and low emigration rate μ, then accept The implemented mutation mechanism is problem
more species from HSI islands to move to their islands, dependent. In which a new region are created by hybrid
which may lead to increase in the suitability index of the others region [6].
island. The immigration and emigration rates depend on For j=1 to length (SIV) do
the number of species in the habitats [5]. Use λi and μi to compute the probability Pi
Like other Evolutionary Algorithms, Biogeography Based Select a variable Ii (SIV) with probability
Optimization operates probabilistically. The probability based on Pi
that an individual shares a feature with the rest of the If Ii (SIV) selected then
population is proportional to its fitness. The probability
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All Rights Reserved © 2012 IJARCSEE
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International Journal of Advanced Research in Computer Science and Electronics Engineering
Volume 1, Issue 4, June 2012
Replace Ii (SIV) with a randomly • Algorithms and transformations [9].
generated SIV Audio Steganography
End if In a computer-based audio steganography system, secret
End for messages are embedded in digital sound. The secret
Following are Biogeography Based Algorithm:- message is embedded by slightly altering the binary
Initialize a set of solutions to a problem sequence of a sound file. Existing audio steganography
Compute ―fitness‖ (HSI) for each solution software can embed messages in WAV, AU, and even
Compute S, , and for each solution MP3 sound files .Embedding secret messages in digital
sound is usually a more difficult process than embedding
Modify habitats (migration) based on ,
messages in other media, such as digital images[9].
Mutation
Protocol Steganography
Typically we implement elitism The term protocol steganography refers to the technique of
Go to step 2 for the next iteration if needed embedding information within messages and network
control protocols used in network transmission. In the
II. REVIEW ON LITERATURE
layers of the OSI network model there exist covert
―Steganography‖ word ancient origins can be traced back
channels where steganography can be used. An example of
to 440 BC. Although the term steganography was only
where information can be hidden is in the header of a TCP/
coined at the end of the 15th century, the use of
IP packet in some fields that are either optional or are
steganography dates back several millennia. In ancient
never used [9].
times, messages were hidden on the back of wax writing
tables, written on the stomachs of rabbits, or tattooed on
III. DESIGN AND IMPLEMENTATION:-
the scalp of slaves. Invisible ink has been in use for
centuries—for fun by children and students and for serious
To implement Biogeography Based Optimization for
espionage by spies and terrorists. Depending on the type of
image steganography, some stages are widely used. The
the cover object, definite and appropriate technique is
main stages are Image segmentation, embedding image
followed in order to obtain security. In this section, we will
and apply Biogeography Based Optimization strategy.
discuss different techniques or methods which are often
The image first considered is a color image. An image is
used in image, audio and video steganography[8].
an array of numbers that represent light intensities at
Text Steganography various points (pixels). These pixels make up the image’s
Many techniques involve the modification of the layout of
raster data. A common image size is 640 × 480 pixels and
a text, rules like using every n-th character or the altering
256 colors (or 8 bits per pixel). Such an image could
of the amount of white space after lines or between words.
contain about 300 kilobits of data. The secret message was
The last technique was successfully used in practice and
first split into partitions, while the cover image was
even after a text has been printed and copied on paper for
divided into blocks of size and BBO was used to convert
ten times, the secret message could still be retrieved.
the blocks from spatial domain to frequency domain on the
Another possible way of storing a secret inside a text is
basis of biogeography concepts. Then, biogeography based
using a publicly available cover source, a book or a
optimization (BBO) algorithm was applied to search for an
newspaper, and using a code which consists for example of
optimal substitution matrix to transform the split partitions
a combination of a page number, a line number and a
for an optimal embedding. Next, the transformed part of
character number. This way, no information stored inside
secret message was embedded into the coefficients of the
the cover source will lead to the hidden message.
transformed image blocks. Experimental results show the
Discovering it relies solely on gaining knowledge of the
proposed method can keep the quality of the stego-image
secret key [8].
better, while the security of the hidden secret message is
Image Steganography increased by use of the substitution matrix.
To hide information, straight message insertion may
The steps of the complete process used in the present work
encode every bit of information in the image or selectively
embed the message in ―noisy‖ areas that draw less A. Proposed Algorithm
attention—those areas where there is a great deal of natural Image Segmentation is one of the important aspects of
color variation. The message may also be scattered Digital image processing. Color Image Segmentation is a
randomly throughout the image. A number of ways exist to process of extracting the image domain form one or more
hide information in digital media. Common approaches connected regions satisfying uniformity criterion which is
include based on features derived from spectral components. The
• Least significant bit insertion image first considered is a color image; color image is
• Masking and filtering taken because this image is further divided into different
• Redundant Pattern Encoding color spaces. Fabric.jpg RGB image is taken as an input,
• Encrypt and Scatter which is an image of colorful fabric that consists of five
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- 4. ISSN: 2277 – 9043
International Journal of Advanced Research in Computer Science and Electronics Engineering
Volume 1, Issue 4, June 2012
different colors. Fabric.jpg RGB image is taken as an If Hj is selected
input, which is an image of colorful fabric that consists of Randomly select an SIV from Hj
five different colors. After Image segmentation, applying Replace a random SIV in Hi with ά μi
BBO approach on the cover image and set probability. For End
each region Migrate and Mutation the pixels compute End
coding (.m file). This step adjusts the saturated pixel End
components in a way to guarantee that they do not exceed Step6: The Embedding process
their maximum value due to modifying their corresponding Next, step is the embedding process of the proposed
coefficients. algorithm. Of course, we need first to convert the secret
The Proposed algorithm can be summarized as follows: message into a 1D bit stream. The details of this step will
Step 1: Take a fabric.jpg image as a cover image and depend on the particular message type. The next step that
fabric1.jpg image as a hidden image. follows the cover adjustment is concerned with applying
Step 2: Using Region Growing based Image Segmentation biogeography Based optimization (BBO) on the cover
criteria selecting a seed point from cover image. After image. The embedding process stores (N) message bits in
selecting, then examine their neighboring pixels of seed the least significant bits (LSB) of the cover image. After
points based on some predefined criteria and generate the embedding process ends the stego image is produced
appropriate cluster. by applying the optimization technique.
Step 3: Classify Each Pixel Using the Nearest Neighbor Step7: The Extraction Module
Rule. Calculate CMC color distances are used between
The extraction process reverses the embedding operation
pixels that compute the distance between two pixels that
starting from applying the BBO on each color plane of the
have same characteristics.
stego image, then selecting the embedded coefficients,
Step 4: Cover Adjustment
until extracting the embedded message bits from the N
Before the embedding process takes place we need first to
LSB's of the integer coefficients. Furthermore, the
apply a pre-processing step on the cover image. This is a
extracted bits are converted into its original digital form.
very important step to preserve the overall invert ability of
Step8: Stego Image is produced as an output.
the transform. That is, the embedding process may modify
a coefficient that corresponds to a saturated pixel color
component in such a way that makes it exceed its
maximum value. In this case higher values will be clipped Image as an Input
and the embedded message bits would then be lost. Hence,
the original cover pixels components (H (i, j, k)) are
adjusted according to the formula shown below .It contain Generating clusters from
the number of bits to be embedded in each coefficient.
This adjustment guarantees that the reconstructed pixels cover Image
from the embedded coefficients would not exceed the
maximum value and hence the message will be recovered
correctly. Set probability for each region. Probability is Set Probability for each
like a threshold values. The probability Ps the region pixel
contains exactly S pixels. Ps changes from time to time as
follows:
Ps (t+Δt)= Ps(t)(1-λs Δt-μs Δt)+ Ps-1 λs-1+ Ps+1μs+1 Δt) Cover adjustment using
Step 5: HSI (highly suitability index) that contain pixels biogeography parameters
which have more similar properties. Low suitability index
(LSI) that contain pixels which contain pixels that not so
familiar that depends upon the probability produced.
Embedding images
Region modification can loosely be described as follows:
H is a probabilistic operator that adjusts habitat H based
on the ecosystem Hn. The probability that is H modified is
proportional to its immigration rate λ, and the probability Extraction Process
that the source of the modification comes from Hj is
proportional to the emigration rate μj
Region modification can loosely be described as follows:
Select Hi with probability ά λi Output Image
If Hi is selected
For j=1 to
Select Hj with probability ά μi Figure 5:-Proposed Algorithm
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International Journal of Advanced Research in Computer Science and Electronics Engineering
Volume 1, Issue 4, June 2012
IV.RESULTS AND DISCUSSION This cluster contains the higher level of magenta color
with the some percentage of yellow color from the fabric
For Image Steganography, a jpg Fabric color image is image as shown in figure 9.
taken as an input image. Color image gives detailed
information about the fabric image and an attractive way
of producing an image. JPG are the image compression
standard that define procedures for compressing and
decompressing images for reducing the amount of data
needed to represent an image.JPG images consist of 680 *
500 pixels with bit depth 48.An image basically consists of
five color objects. Biogeography Based optimization
applied into image to extract red, green, purple, magenta
and yellow color objects using cluster index as shown in
figure 6.
Following results are generated:
Figure 9:- Cluster 3
Above shown cluster are merged into one fabric image as
shown in figure. Fabric image is RGB image that contain
red, green and blue color in collection.
Figure 6:-Fabric Image with Index
Image segmentation has done to generate cluster of similar
colors .as shown in the below figure7 cluster contain the
yellow and black color of similar image.
Figure 10:- Fabric Image
After creating the cluster of a image. Cover image as
shown in Figure is taken. The first is the innocent-looking
image that will hold the hidden information, called the
cover image. Biogeography Based optimization technique
is applied into this image. , the original cover pixels
components (H (i, j, k)) are adjusted according to the
formula shown below .It contain the number of bits to be
embedded in each coefficient. This adjustment guarantees
that the reconstructed pixels from the embedded
Figure 7:-Cluster 1 coefficients would not exceed the maximum value and
This cluster contains the higher level of white color from hence the message will be recovered correctly.
the fabric image as shown in figure 8.
Figure 8:-Cluster 2 Figure 11:-Cover Image
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International Journal of Advanced Research in Computer Science and Electronics Engineering
Volume 1, Issue 4, June 2012
Next image is message image. The second file is the VI.CONCLUSION
message—the information to be hidden. A message may Steganography goes well beyond simply embedding text in
be plain text, cipher text, other images, or anything that an image. It also pertains to other media, including voice,
can be embedded in a bit stream. text, binary files, and communication channels. Color
images allow for more reliable Image Steganography than
for gray scale images. Digital image steganography and its
derivatives are growing in use and application.
Steganography’s ease of use and availability has law
enforcement concerned with trafficking of illicit material
via Web page images, audio, and other files. As concluded,
Biogeography Based Optimization is more reliable and fast
search algorithm for Image Steganography purposes.
Biogeography Based Optimization generally results in
better optimization results than the evolutionary Algorithm
for the problems that we investigate. Biogeography based
Image Steganography produce different cluster of different
color at higher computational time. For the future work the
Image Segmentation techniques or noise removal methods
can be improved, so that the input image to be extracted
could be made better which can improve the final
outcome.
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