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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


                                                                                                                                 25
                                                All Rights Reserved © 2012 IJARCSEE
ISSN: 2277 – 9043
                         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




                                                                                                                               26
                                              All Rights Reserved © 2012 IJARCSEE
ISSN: 2277 – 9043
                        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




                                                                                                                           27
                                             All Rights Reserved © 2012 IJARCSEE
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



                                                                                                                         28
                                            All Rights Reserved © 2012 IJARCSEE
ISSN: 2277 – 9043
                        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



                                                                                                                       29
                                            All Rights Reserved © 2012 IJARCSEE
ISSN: 2277 – 9043
                       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.
                                                                                   REFERENCES
                                                              [1] R.J.Anderson and F.A.P. Petitcolas, ―On the Limits of
                                                              Steganography,‖ J. Selected Areas in Comm., vol. 16, no.
                 Figure 12:-Hide Image                        4,1998, pp. 474–481.
When combined, the cover image and the embedded               [2]    N.F.Johnson      and      S.   Jajodia,   ―Exploring
message make a stego image. A stego-key (a type of            Steganography: Seeing the Unseen,‖ Computer, vol. 31,
password) may also be used to hide, and then later decode,    no. 2, 1998, pp.26–34.
the message. At last stego image is generated.                [3]    N.Provos      and     P.     Honeyman,     ―Detecting
                                                              Steganographic Content on the Internet,‖ Proc. 2002
                                                              Network and Distributed System Security Symp., Internet
                                                              Soc., 2002.
                                                              [4] Pichel, J.C., Singh D.E., Rivera, F.F. (2006), ―Image
                                                              Segmentation       Based      on     Merging     Suboptimal
                                                              Segmentations‟ , Pattern Recognition Letters, Vol. 27.
                                                              [5]     Simon,      D.    (2008),      ―Biogeography-Based
                                                              Optimization,‖ IEEE Transactions on Evolutionary
                                                              Computation, Vol. 12, No. 6, pp. 702 – 713.
                                                              [6] Haiping, Ma., et al. (2009), ―Equilibrium Species
                                                              Counts and Migration Model Tradeoffs for Biogeography-
                                                              Based Optimization‖, Joint 48th IEEE Conference on
                                                              Decision and Control and 28th Chinese Control
                 Figure 13:-Stego Image                       Conference Shanghai, P.R. China, pp. 897-900.
                                                              [7] Auger, A., et al. (2005), ―Performance Evaluation of
           V:-PERFORMANCE EVALUATION                          an Advanced Local Search Evolutionary Algorithm‖, In
In BBO, a solution is represented by an island. Islands       Proceedings of the IEEE Congress on Evolutionary
consist of solution features named suitability index          Computation, Vol. 23, pp. 789-802.
variables (SIV). The aim of optimization is to optimal        [8] Mohammad Tanvir Parvez and Adnan Gutub , ―RGB
solution in terms of the variables of the problem. An array   Intensity Based Variable-Bits Image Steganography‖
of variable values to be optimized is formed. In BBO the      ,APSCC 2008-Proceedings of 3rd IEEE Asia-Pacific
term ―island" is used for this array. The performance of      Service Computing Conference, Yilan Taiwan, 9-12
implementing color image steganography using BBO              December 2008.
approach is compared with Existing evolutionary               [9] Shi Lee ,Wen-Hsiang Tsai, “Data hiding in grayscale
algorithm in terms of computational speed. The                images by dynamic programming based on a human visual
computational time of above used image is much faster         model‖ ,journal of Pattern Recognition Volume 42, Issue
than the genetic algorithm in terms of 21 seconds.            7, pp 1604-1611, July 2009.




                                                                                                                       30
                                           All Rights Reserved © 2012 IJARCSEE

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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 25 All Rights Reserved © 2012 IJARCSEE
  • 2. ISSN: 2277 – 9043 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 26 All Rights Reserved © 2012 IJARCSEE
  • 3. ISSN: 2277 – 9043 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 27 All Rights Reserved © 2012 IJARCSEE
  • 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 28 All Rights Reserved © 2012 IJARCSEE
  • 5. ISSN: 2277 – 9043 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 29 All Rights Reserved © 2012 IJARCSEE
  • 6. ISSN: 2277 – 9043 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. REFERENCES [1] R.J.Anderson and F.A.P. Petitcolas, ―On the Limits of Steganography,‖ J. Selected Areas in Comm., vol. 16, no. Figure 12:-Hide Image 4,1998, pp. 474–481. When combined, the cover image and the embedded [2] N.F.Johnson and S. Jajodia, ―Exploring message make a stego image. A stego-key (a type of Steganography: Seeing the Unseen,‖ Computer, vol. 31, password) may also be used to hide, and then later decode, no. 2, 1998, pp.26–34. the message. At last stego image is generated. [3] N.Provos and P. Honeyman, ―Detecting Steganographic Content on the Internet,‖ Proc. 2002 Network and Distributed System Security Symp., Internet Soc., 2002. [4] Pichel, J.C., Singh D.E., Rivera, F.F. (2006), ―Image Segmentation Based on Merging Suboptimal Segmentations‟ , Pattern Recognition Letters, Vol. 27. [5] Simon, D. (2008), ―Biogeography-Based Optimization,‖ IEEE Transactions on Evolutionary Computation, Vol. 12, No. 6, pp. 702 – 713. [6] Haiping, Ma., et al. (2009), ―Equilibrium Species Counts and Migration Model Tradeoffs for Biogeography- Based Optimization‖, Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Figure 13:-Stego Image Conference Shanghai, P.R. China, pp. 897-900. [7] Auger, A., et al. (2005), ―Performance Evaluation of V:-PERFORMANCE EVALUATION an Advanced Local Search Evolutionary Algorithm‖, In In BBO, a solution is represented by an island. Islands Proceedings of the IEEE Congress on Evolutionary consist of solution features named suitability index Computation, Vol. 23, pp. 789-802. variables (SIV). The aim of optimization is to optimal [8] Mohammad Tanvir Parvez and Adnan Gutub , ―RGB solution in terms of the variables of the problem. An array Intensity Based Variable-Bits Image Steganography‖ of variable values to be optimized is formed. In BBO the ,APSCC 2008-Proceedings of 3rd IEEE Asia-Pacific term ―island" is used for this array. The performance of Service Computing Conference, Yilan Taiwan, 9-12 implementing color image steganography using BBO December 2008. approach is compared with Existing evolutionary [9] Shi Lee ,Wen-Hsiang Tsai, “Data hiding in grayscale algorithm in terms of computational speed. The images by dynamic programming based on a human visual computational time of above used image is much faster model‖ ,journal of Pattern Recognition Volume 42, Issue than the genetic algorithm in terms of 21 seconds. 7, pp 1604-1611, July 2009. 30 All Rights Reserved © 2012 IJARCSEE