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  1. 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 advantageimage, audio, video or TCP/IP header file. Steganography’s of steganography is that those who are outside the partygoal is to hide the existence of embedded data so as not to even do not realize that some sort of communication isarouse an eavesdropper’s suspicion. For hiding secret data indigital images, large varieties of steganographic techniques being done [2].are available, some are more complex than others, and all ofthem have their respective pros and cons. This paper intendsto give thorough understanding and evolution ofbiogeography based optimization technique for data hiding.It covers and integrates recent research work without goingin to much detail of steganalysis, which is the art and scienceof defeating steganography. In addition, our proposedmethod computes performance evaluation in terms ofcomputational time of 21.2 seconds as compared to otherevolutionary algorithm. It has good optimizationperformance due to its migration operator. Therefore,Biogeography Based technique is more reliable and faster for Secret Image Cover Image Stego ImageImage Steganography.Index Terms: - Biogeography, Image segmentation, RGB FIGURE. 1: The block diagram of a simple(Red, Green and Blue) model, Steganography, Computationaltime. steganographic system The main applications of such a scheme are to transmit I. INTRODUCTION secret data. In the second application, a short message (aTo 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 andInformation hiding is an old but interesting technology. a content-based method can be efficiently applied toSteganography is a branch of information hiding in which watermark digital images [3]. The stego-images generatedsecret information is camouflaged within other by these methods can survive common image processinginformation. 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 ofsteganography is to communicate securely in such a waythat the true message is not visible to the observer. That isunwanted parties should not be able to distinguish anysense between cover-image (image not containing anysecret message) and stego-image (modified cover-imagethat containing secret message). Thus the stego-imageshould not deviate much from original cover-image.figure.1 shows the block diagram of a simple imagesteganographic system. Depending on the form of type ofinformation hidden in digital images, data hiding schemescan 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. 2. ISSN: 2277 – 9043 International Journal of Advanced Research in Computer Science and Electronics Engineering Volume 1, Issue 4, June 20121.1. 1 Applications of Steganography that an individual receives a feature from the rest of theSteganography 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 givensecret data, in military and intelligence agencies, in smart λ = I (1-K/n)identity cards where personal details are embedded in the µ=E/nphotograph itself for copyright control of materials [3]. In Where I is the maximum possible immigration rate; E ismedical imaging, patient’s details are embedded within the maximum possible emigration rate; k is the number ofimage providing protection of information and reducing species of the k-th individual; n is the maximum number oftransmission time and cost1, in online voting system so as species.to make the online election secure and robust against a Emigrating Islandsvariety of fraudulent behaviours2, for data hiding incountries where cryptography is prohibited, in improvingmobile banking security3, in tamper proofing so as toprevent or detect unauthorized modifications and othernumerous applications [8] as shown in figure3. Steganogarphy ApplicationsCopy 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) MigrationBiogeography Based Optimization (BBO) is a recently The BBO migration strategy in which many parents candeveloped heuristic algorithm which has shown impressive contribute to a single offspring, but it differs in at least oneperformance on many well known benchmarks. important aspect. BBO migration is used to changeBiogeography Based Optimization is based on the existing habitat [6].mathematical study of biogeography as shown in figure For i= 1 to NP do4[5]. Each island has its characteristics such as food Select Ii with probability based on λ iavailability, rainfall, temperature, diversity of species, If Ii is selected thensecurity, population of species etc. The quality of an island For j=1 to NP dois measured by its suitability index (SI). Islands with HSI Select Ij with probability based on μ jare more suitable for living and therefore have large If Ij is selectedpopulation while those with LSI have sparse population Randomly select a SIV v from I jdue to the fact that of suitability or friendly for living. HSI Replace a random SIV in Ii with vislands have low immigration rate λ and high emigration End ifrate μ simply due to high population.HSI has less dynamic. End forBy the same virtue, islands with LSI have high b) Mutationimmigration rate λ ¸ and low emigration rate μ, then accept The implemented mutation mechanism is problemmore species from HSI islands to move to their islands, dependent. In which a new region are created by hybridwhich 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) dothe number of species in the habitats [5]. Use λi and μi to compute the probability PiLike other Evolutionary Algorithms, Biogeography Based Select a variable Ii (SIV) with probabilityOptimization operates probabilistically. The probability based on Pithat an individual shares a feature with the rest of the If Ii (SIV) selected thenpopulation is proportional to its fitness. The probability 26 All Rights Reserved © 2012 IJARCSEE
  3. 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 secretFollowing 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 ofto 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 aresteganography dates back several millennia. In ancient never used [9].times, messages were hidden on the back of wax writingtables, written on the stomachs of rabbits, or tattooed on III. DESIGN AND IMPLEMENTATION:-the scalp of slaves. Invisible ink has been in use forcenturies—for fun by children and students and for serious To implement Biogeography Based Optimization forespionage by spies and terrorists. Depending on the type of image steganography, some stages are widely used. Thethe cover object, definite and appropriate technique is main stages are Image segmentation, embedding imagefollowed 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 isused in image, audio and video steganography[8]. an array of numbers that represent light intensities atText Steganography various points (pixels). These pixels make up the image’sMany techniques involve the modification of the layout of raster data. A common image size is 640 × 480 pixels anda text, rules like using every n-th character or the altering 256 colors (or 8 bits per pixel). Such an image couldof the amount of white space after lines or between words. contain about 300 kilobits of data. The secret message wasThe last technique was successfully used in practice and first split into partitions, while the cover image waseven after a text has been printed and copied on paper for divided into blocks of size and BBO was used to convertten times, the secret message could still be retrieved. the blocks from spatial domain to frequency domain on theAnother possible way of storing a secret inside a text is basis of biogeography concepts. Then, biogeography basedusing a publicly available cover source, a book or a optimization (BBO) algorithm was applied to search for annewspaper, and using a code which consists for example of optimal substitution matrix to transform the split partitionsa combination of a page number, a line number and a for an optimal embedding. Next, the transformed part ofcharacter number. This way, no information stored inside secret message was embedded into the coefficients of thethe cover source will lead to the hidden message. transformed image blocks. Experimental results show theDiscovering it relies solely on gaining knowledge of the proposed method can keep the quality of the stego-imagesecret key [8]. better, while the security of the hidden secret message isImage 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 workencode every bit of information in the image or selectivelyembed the message in ―noisy‖ areas that draw less A. Proposed Algorithmattention—those areas where there is a great deal of natural Image Segmentation is one of the important aspects ofcolor variation. The message may also be scattered Digital image processing. Color Image Segmentation is arandomly throughout the image. A number of ways exist to process of extracting the image domain form one or morehide information in digital media. Common approaches connected regions satisfying uniformity criterion which isinclude 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. 4. ISSN: 2277 – 9043 International Journal of Advanced Research in Computer Science and Electronics Engineering Volume 1, Issue 4, June 2012different colors. Fabric.jpg RGB image is taken as an If Hj is selectedinput, which is an image of colorful fabric that consists of Randomly select an SIV from Hjfive different colors. After Image segmentation, applying Replace a random SIV in Hi with ά μiBBO approach on the cover image and set probability. For Endeach region Migrate and Mutation the pixels compute Endcoding (.m file). This step adjusts the saturated pixel Endcomponents in a way to guarantee that they do not exceed Step6: The Embedding processtheir maximum value due to modifying their corresponding Next, step is the embedding process of the proposedcoefficients. algorithm. Of course, we need first to convert the secretThe Proposed algorithm can be summarized as follows: message into a 1D bit stream. The details of this step willStep 1: Take a fabric.jpg image as a cover image and depend on the particular message type. The next step thatfabric1.jpg image as a hidden image. follows the cover adjustment is concerned with applyingStep 2: Using Region Growing based Image Segmentation biogeography Based optimization (BBO) on the covercriteria selecting a seed point from cover image. After image. The embedding process stores (N) message bits inselecting, then examine their neighboring pixels of seed the least significant bits (LSB) of the cover image. Afterpoints based on some predefined criteria and generate the embedding process ends the stego image is producedappropriate cluster. by applying the optimization technique.Step 3: Classify Each Pixel Using the Nearest Neighbor Step7: The Extraction ModuleRule. Calculate CMC color distances are used between The extraction process reverses the embedding operationpixels that compute the distance between two pixels that starting from applying the BBO on each color plane of thehave same characteristics. stego image, then selecting the embedded coefficients,Step 4: Cover Adjustment until extracting the embedded message bits from the NBefore the embedding process takes place we need first to LSBs of the integer coefficients. Furthermore, theapply 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 modifya coefficient that corresponds to a saturated pixel colorcomponent in such a way that makes it exceed itsmaximum value. In this case higher values will be clipped Image as an Inputand the embedded message bits would then be lost. Hence,the original cover pixels components (H (i, j, k)) areadjusted according to the formula shown below .It contain Generating clusters fromthe number of bits to be embedded in each coefficient.This adjustment guarantees that the reconstructed pixels cover Imagefrom the embedded coefficients would not exceed themaximum value and hence the message will be recoveredcorrectly. Set probability for each region. Probability is Set Probability for eachlike a threshold values. The probability Ps the region pixelcontains exactly S pixels. Ps changes from time to time asfollows: Ps (t+Δt)= Ps(t)(1-λs Δt-μs Δt)+ Ps-1 λs-1+ Ps+1μs+1 Δt) Cover adjustment usingStep 5: HSI (highly suitability index) that contain pixels biogeography parameterswhich have more similar properties. Low suitability index(LSI) that contain pixels which contain pixels that not sofamiliar that depends upon the probability produced. Embedding imagesRegion modification can loosely be described as follows:H is a probabilistic operator that adjusts habitat H basedon the ecosystem Hn. The probability that is H modified isproportional to its immigration rate λ, and the probability Extraction Processthat the source of the modification comes from Hj isproportional to the emigration rate μjRegion 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. 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 fabricFor Image Steganography, a jpg Fabric color image is image as shown in figure 9.taken as an input image. Color image gives detailedinformation about the fabric image and an attractive wayof producing an image. JPG are the image compressionstandard that define procedures for compressing anddecompressing images for reducing the amount of dataneeded to represent an image.JPG images consist of 680 *500 pixels with bit depth 48.An image basically consists offive color objects. Biogeography Based optimizationapplied into image to extract red, green, purple, magentaand yellow color objects using cluster index as shown infigure 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 IndexImage segmentation has done to generate cluster of similarcolors .as shown in the below figure7 cluster contain theyellow 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 andThis 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. 6. ISSN: 2277 – 9043 International Journal of Advanced Research in Computer Science and Electronics Engineering Volume 1, Issue 4, June 2012Next image is message image. The second file is the VI.CONCLUSIONmessage—the information to be hidden. A message may Steganography goes well beyond simply embedding text inbe 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, ―Exploringmessage 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‖, InIn BBO, a solution is represented by an island. Islands Proceedings of the IEEE Congress on Evolutionaryconsist 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 , ―RGBsolution 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-Pacificterm ―island" is used for this array. The performance of Service Computing Conference, Yilan Taiwan, 9-12implementing color image steganography using BBO December 2008.approach is compared with Existing evolutionary [9] Shi Lee ,Wen-Hsiang Tsai, “Data hiding in grayscalealgorithm in terms of computational speed. The images by dynamic programming based on a human visualcomputational time of above used image is much faster model‖ ,journal of Pattern Recognition Volume 42, Issuethan the genetic algorithm in terms of 21 seconds. 7, pp 1604-1611, July 2009. 30 All Rights Reserved © 2012 IJARCSEE

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