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(IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 2, 2010 Comparative Performance of Information Hiding in Vector Quantized Codebooks using LBG, KPE, KMCG and KFCG Dr. H. B. Kekre Archana Athawale Tanuja K. Sarode Kalpana Sagvekar Senior Professor, Ph.D. Scholar, MPSTME, Ph.D. Scholar, MPSTME, Lecturer, MPSTME, NMIMS University, NMIMS University, Fr. Conceicao Rodrigues NMIMS University, Vileparle(W), Mumbai-56 Vileparle(W), Mumbai-56 COE, Bandra(W), Vile-parle(W), Assistant Professor, TSEC, Assistant Professor, TSEC, Mumbai-50, India Mumbai-56, India. Bandra(W), Mumbai-50, Bandra(W), Mumbai-50, kalpanasagvekar@gmail.c hbkekre@yahoo.com India. India. om Abstract - In traditional VQ - data hiding schemes secret data is appear on the picture to catch malicious attackers‟ attention. hidden inside index based cover image resulting in limited embedding Thereupon, the security of the secret information is ensured capacity. To improve the embedding capacity as well as to have against detection. As for the payload capacity limit, it minimum distortion to carrier media, we have proposed one novel evaluates the power of a data-hiding scheme by checking method of hiding secret data into the codebook. In this paper we have how big the maximum amount of the secret information is used four different algorithms Linde Buzo and Gray (LBG), Kekre’s that can be hidden in the cover media. Generally speaking, Proportionate Error (KPE), Kekre’s Median Codebook Generation the larger the payload size is, the worse the stego-image algorithm (KMCG) and Kekre’s Fast Codebook Generation Algorithm (KFCG) to prepare codebooks. It is observed that KFCG gives visual quality will be. That is to say, in the world of data minimum distortion. hiding, how to strike this balance and settle on an ideal robustness-capacity tradeoff is maybe the core problem to solve. Keywords - Reversible (lossless) data hiding, VQ, LBG, KPE, KMCG, The existing schemes of data hiding can roughly be classified KFCG. into the following three categories: Spatial domain data hiding [2],[3],[4]: Data hiding of this I. INTRODUCTION type directly adjust image pixels in the spatial domain for data embedding. This technique is simple to implement, Due to the digitalization of all kinds of data and the amazing offering a relatively high hiding capacity. The quality of the development of network communication, information security stego image can be easily controlled. Therefore, data hiding over the Internet has become more and more important. The of this type has become a well known method for image Internet is basically a giant open channel with security steganography. problems like modifications and interceptions occurring at any time in any place. Under such circumstances, quite some Frequency domain data hiding [5],[6]: In this method images different approaches have been proposed in an attempt to make are first transformed into frequency domain, and then data is private communication secure. Researchers have developed embedded by modifying the transformed coefficients. schemes where the secret message is protected by getting transformed into the form of a stack of seemingly meaningless Compressed domain data hiding [7],[8]: Data hiding is data, which only the authorized user can retransform back to its obtained by modifying the coefficients of the compressed original form by way of some secret information. However, the code of a cover image. Since most images transmitted over appearance of a stack of seemingly meaningless data could be Internet are in compressed format, embedding secret data into an irresistible attraction to an attacker with a desire to recover the compressed domain would provoke little suspicion. the original message. Another approach, called steganography, hides the secret message in some cover material with a Due to the restricted bandwidth of networks, we cannot keep common appearance to avoid suspicion. The data-hiding up with the growing sizes of various multimedia files. Many efficacy can be judged according to two criteria: (1) visual popular image compression algorithms have been proposed quality (2) payload capacity limit. The term “visual quality” to respond this problem, such as VQ [15], side match VQ here refers to the quality of the stego-image. That is to say, only (SMVQ) [16], JPEG [17], JPEG2000 [18], and so on. One of a limited number of distortions within limited areas are allowed the most commonly studied image compression techniques is in the stego-image so that no obvious traces of modification Vector Quantization (VQ) [19], which is an attractive choice 89 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 2, 2010 because of its simplicity and cost-effective implementation. decides the error ratio. Hereafter the procedure is same as Indeed, a variety of VQ techniques have been successfully that of LBG. While adding proportionate error a safe guard is applied in real applications such as speech and image coding also introduced so that neither v1 nor v2 go beyond the [20], [22], VQ has faster encode/decode time along with training vector space. This removes the disadvantage of the simpler framework compared to JPEG/JPEG2000. Vector LBG. Both LBG and KPE requires 2M number of Euclidean Quantization requires limited information during decoding and distance computations and 2M number of comparisons where works best in applications in which the decoder has only M is the total number of training vectors in every iteration to limited information [21]. generate clusters. c. Kekre’s Median Codebook Generation Algorithm There are two approaches for hiding data into VQ compressed (KMCG) [13] domain; either hides the covert data into index based cover image or in codebook. In this paper we have proposed a method In this algorithm image is divided in to blocks and blocks are of hiding data into codebook which is not been explored. In converted to the vectors of size k. The Fig. 2 below section II we present codebook design algorithms. Section III represents matrix T of size M x k consisting of M number of explains proposed search algorithm followed by Section IV in image training vectors of dimension k. Each row of the which results and evaluation is given. Section V gives matrix is the image training vector of dimension k. conclusion. x1,1 x1,2 .... x1,k II. VQ COMPRESSION TECHNIQUE x2,1 x2,2 .... x2,k T . . . . Vector Quantization (VQ) [9-14] is an efficient technique for . . . . data compression [31-34] and is very popular in a variety of xM,1 xM,2 .... xM,k research fields such as data hiding techniques [7,8], image segmentation [23-26], speech data compression [27], content based image retrieval CBIR [28, 29] and face recognition [30]. Fig. 2. Training Vectors A. Codebook Generation Algorithms The training vectors are sorted with respect to the first member of all the vectors i.e. with respect to the first column a. Linde-Buzo-Gray (LBG) Algorithm [9], [10] of the matrix T and the entire matrix is considered as one single cluster. The median of the matrix T is chosen In this algorithm centroid is calculated as the first codevector (codevector) and is put into the codebook, and the size of the for the training set. In Fig. 1 two vectors v1 & v2 are generated codebook is set to one. The matrix is then divided into two by using constant error addition to the codevector. Euclidean equal parts and the each of the part is then again sorted with distances of all the training vectors are computed with vectors respect to the second member of all the training vectors i.e. v1 & v2 and two clusters are formed based on nearest of v1 or with respect to the second column of the matrix T and we v2. This procedure is repeated for every cluster. The drawback obtain two clusters both consisting of equal number of of this algorithm is that the cluster elongation is –45o to training vectors. The median of both the parts is the picked horizontal axis in two dimensional cases. Resulting in up and written to the codebook, now the size of the codebook inefficient clustering. is increased to two consisting of two codevectors and again each part is further divided to half. Each of the above four parts obtained are sorted with respect to the third column of the matrix T and four clusters are obtained and accordingly four codevectors are obtained. The above process is repeated till we obtain the codebook of desired size. Here quick sort algorithm is used and from the results it is observed that this algorithm takes least time to generate codebook, since Euclidean distance computation is not required. d. Kekre’s Fast Codebook Generation (KFCG) Algorithm In [14], KFCG algorithm for image data compression is Fig.1 LBG for 2 dimensional case proposed. This algorithm reduces the time for codebook generation. It does not use Euclidian distance for codebook b. Proportionate Error Algorithm (KPE) [11], [12] generation. In this algorithm image is divided in to blocks and blocks are converted to the vectors of size k. Initially we Here proportionate error is added to the centroid to generate have one cluster with the entire training vectors and the two vectors v1 & v2. Magnitude of elements of the centroid codevector C1 which is centroid. 90 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 2, 2010 In the first iteration of the algorithm, the clusters are formed by A. Embedding Procedure comparing first element of training vector with first element of code vector C1. The vector Xi is grouped into the cluster 1 if xi1< c11 otherwise vector Xi is grouped into cluster 2 as shown Divide the image into 2×2 block of pixels in Figure. 3a. where codevector dimension space is 2. window In second iteration, the cluster 1 is split into two by comparing Generate initial cluster of training set using the second element xi2 of vector Xi belonging to cluster 1 with that rows of 12 values per pixel window of the second element of the codevector which is centroid of cluster 1. Cluster 2 is split into two by comparing the second element xi2 of vector Xi belonging to cluster 2 with that of the second element of the codevector which is centroid of cluster Apply codebook generation algorithm 2, as shown in Figure. 3b. LBG/KPE/KFCG/KMCG on initial cluster to obtain codebook of size 2048 codevectors This procedure is repeated till the codebook size is reached to the size specified by user. It is observed that this algorithm gives less error as compared to LBG and requires least time to generate codebook as compared to other algorithms, as it does Embed every bit of each pixel in the LSB‟s of not require computation of Euclidian distance. (i.e. 1, 2, 3, 4, variable bit method) each element of codevector belonging to CB Modified CB Generate Index based cover image 3(a). First Iteration B. Extraction & Recovery Procedure Modified CB Index based cover image Extract secret Reconstruct the original data from image by replacing LSB of every each index by 3(b) Second Iteration corresponding element of CB codevector Fig. 3. KFCG algorithm for 2-D case III. PROPOSED APPROACH C. Variable Bit Hiding Algorithm In this approach, we are hiding the secret data into codebook For variable bit hiding Kekre‟s algorithm [2] is used. generated using various codebook generation algorithm such as LBG[10][11], KPE[12][13], KMCG[14], KFCG[15]. There are 1. If the value of codebook vector element is in the range various ways of hiding: 1bit, 2 bits, 3 bits, 4 bits & variable bits 240≤gi≤255 then we embed 4 bits of secret data into the hiding. 4 LSB‟s codebook vector element. This can be done by observing the 4 most significant bits (MSB‟s). If they are all 1‟s then the remaining 4 LSB‟s can be used for embedding data. 2. If the value of codebook vector element is in the range 224≤gi≤239 then we embed 3 bits of secret data. . This 91 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 2, 2010 can be done by observing the 3 most significant bits (MSB‟s). If they are all 1‟s then the remaining 3 LSB‟s can be used for embedding data. 3. If the value of codebook vector element is in the range 192≤gi≤223 then we embed 2 bits of secret data. . This can be done by observing the 2 most significant bits (MSB‟s). If they are all 1‟s then the remaining 2 LSB‟s can be used 5 (a) 5 (b) 5 (c) for embedding data. Original Secret Reconstructed image using 4. If the value of codebook vector element is in the range Cover image Message KFCG for Variable bits Fern.bmp Method 0≤gi≤191 we embed 1 bit of secret data. 60 50 40 IV. RESULTS & EVALUATIONS MSE 30 20 In our proposed approach, we have generated codebook using 10 0 LBG, KPE, KMCG and KFCG for 24 bit color image of size KPE KPE KPE KPE KPE LBG KMCG LBG KMCG LBG KMCG LBG KMCG LBG KMCG KFCG KFCG KFCG KFCG KFCG 256×256 shown in Fig. 4 & 5. Codebook is of size 2048×12 (i.e. 2048 code vectors each contains 12 bytes - 4 pairs of 1 bit 2 bits 3 bits 4 bits variable bits RGB). We have hidden 32×32 gray image. Hiding Capacity Fig. 4. to Fig. 8. Shows the results of 1bit, 2bits 3bits 4bits and 5 (d) plot of Hiding Capacity versus MSE Variable bits using codebook obtained from LBG, KPE, Fig. 5. Results of 1bit, 2bits 3bits 4bits and Variable bits on the cover image KMCG and KFCG on the various cover images Bird, Fern, Fern shown in Fig.5(a) and secrete image shown in Fig. 5(b). Puppy, Cat and Temple hiding same secrete image for fair comparison respectively. Fig. 9. Shows the plot of Hiding Capacity versus average MSE for various hiding methods 1bit, 2bits 3bits 4bits and Variable bits on LBG, KPE, KMCG and KFCG VQ Codebooks respectively. 6 (a) 6 (b) 6 (c) Original Secret Reconstructed image using Cover image Message KFCG for Variable bits Puppy.bmp Method 30 25 20 MSE 15 4 (a) 4 (b) 4 (c) 10 5 Original Secret Reconstructed image using 0 Cover image Message KFCG for Variable bits KPE KPE KPE KPE KPE LBG KMCG LBG KMCG LBG KMCG LBG KMCG LBG KMCG KFCG KFCG KFCG KFCG KFCG Birds.bmp Method 1 bit 2 bits 3 bits 4 bits variable 80 bits 70 60 Hiding Capacity 50 MSE 40 6 (d) plot of Hiding Capacity versus MSE 30 Fig. 6. Results of 1bit, 2bits 3bits 4bits and Variable bits on the cover image 20 10 Puppy shown in Fig.6(a) and secrete image shown in Fig. 6(b). 0 KPE KPE KPE KPE KPE LBG KMCG LBG KMCG LBG KMCG LBG KMCG LBG KMCG KFCG KFCG KFCG KFCG KFCG 1 bit 2 bits 3 bits 4 bits variable bits Hiding Capacity 4 (d) plot of Hiding Capacity versus MSE Fig. 4. Results of 1bit, 2bits 3bits 4bits and Variable bits on the cover image bird and secrete image shown in Fig. 4(b). 7 (a) 7 (b) 7 (c) Original Secret Reconstructed image using Cover image Message KFCG for Variable bits Cat.bmp Method 92 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 2, 2010 60 60 50 50 Avg. MSE 40 40 MSE 30 30 20 20 10 10 0 0 KPE KPE KPE KPE KPE KPE KPE KPE KPE KPE LBG KMCG LBG KMCG LBG KMCG LBG KMCG LBG KMCG LBG KMCG LBG KMCG LBG KMCG LBG KMCG LBG KMCG KFCG KFCG KFCG KFCG KFCG KFCG KFCG KFCG KFCG KFCG 1 bit 2 bits 3 bits 4 bits variable 1 bit 2 bits 3 bits 4 bits variable bits bits Hiding Capacity Hiding Capacity 7 (d) plot of Hiding Capacity versus MSE Plot of Hiding Capacity versus Avg. MSE Fig. 7. Results of 1bit, 2bits 3bits 4bits and Variable bits on the cover image Cat shown in Fig.7(a) and secrete image shown in Fig. 7(b). Fig. 9. Plot of Hiding Capacity versus average MSE for various hiding methods 1bit, 2bits 3bits 4bits and Variable bits on LBG, KPE, KMCG and KFCG VQ Codebooks respectively. It is observed from Fig. 4 to Fig. 9. that KFCG codebook gives less MSE in all the data hiding methods 1bit, 2bits, 3bits, 4bits and varible bits as compared to LBG, KPE, and KMCG codebook. Further it is observed that varible bit 8 (a) 8 (b) 8 (c) method using KFCG gives the best performance. Original Secret Reconstructed image using Cover image Message KFCG for Variable bits Temple.bmp Method Table 1. Shows the hiding Capacity in bits using 1 bit, 2 bits, 60 3 bits 4 bits, and variable bits method on LBG, KPE, KMCG 50 40 and KFCG codebook of size 2048. MSE 30 20 10 0 KPE KPE KPE KPE KPE LBG KMCG LBG KMCG LBG KMCG LBG KMCG LBG KMCG KFCG KFCG KFCG KFCG KFCG 1 bit 2 bits 3 bits 4 bits variable bits Hiding Capacity 8 (d) plot of Hiding Capacity versus MSE Fig. 8. Results of 1bit, 2bits 3bits 4bits and Variable bits on the cover image Temple shown in Fig.8(a) and secrete image shown in Fig. 8(b). TABLE I. HIDING CAPACITY IN BITS USING 1 BIT, 2 BITS, 3 BITS, 4 BITS, AND VARIABLE BITS METHOD ON LBG, KPE, KMCG AND KFCG CODEBOOK OF SIZE 2048 Hiding Capacity in bits Cover Variable bits Images 1 bit 2 bits 3 bits 4 bits LBG KPE KMCG KFCG Birds 28488 27202 26881 27751 Fern 27561 23891 27646 27965 Puppy 24576 49152 73728 98304 39181 38899 39962 38362 Cat 38076 37891 36364 33940 Temple 26595 26207 25545 26034 From table I it is observed that variable bits give high hiding capacity as compared to 1 bit, 2 bits, 3 bits and 4 bits embedding methods. V. CONCLUSION using MSE as a parameter. It has been observed that KFCG with variable bits for hiding information gives the In this proposed approach the information is hidden in a best performance giving mse equivalent to 2.2 bits per vector quantized codebook by using 1,2,3,4 LSBs of the byte of codevectors. In addition KMCG has very low codevectors. 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Raisoni College of Engineering, Nagpur on 16-18 December of ISTE and also a member of International Association of Engineers 2009, this paper will be uploaded online at IEEE Xplore. (IAENG). Her area of interest is Image Processing, Signal Processing and Computer Graphics. She has about 30 papers in National /International Conferences/Journals to her credit. AUTHORS PROFILE Tanuja K. Sarode has Received Bsc.(Mathematics) from Mumbai Dr. H. B. Kekre has received B.E. (Hons.) in Telecomm. Engineering. University in 1996, Bsc.Tech.(Computer from Jabalpur University in 1958, M.Tech Technology) from Mumbai University in 1999, (Industrial Electronics) from IIT Bombay in 1960, M.E. (Computer Engineering) from Mumbai M.S.Engg. (Electrical Engg.) from University of University in 2004, currently Pursuing Ph.D. from Ottawa in 1965 and Ph.D. (System Identification) Mukesh Patel School of Technology, from IIT Bombay in 1970 He has worked as Management and Engineering, SVKM‟s NMIMS Faculty of Electrical Engg. and then HOD University, Vile-Parle (W), Mumbai, INDIA. She Computer Science and Engg. at IIT Bombay. For has more than 10 years of experience in teaching. Currently working as 13 years he was working as a professor and head in the Department of Assistant Professor in Dept. of Computer Engineering at Thadomal Computer Engg. at Thadomal Shahani Engineering. College, Mumbai. Shahani Engineering College, Mumbai. She is life member of IETE, Now he is Senior Professor at MPSTME, SVKM‟s NMIMS University. member International Association of Engineers (IAENG) and He has guided 17 Ph.Ds, more than 100 M.E./M.Tech and several B.E./ International Association of Computer Science and Information B.Tech projects. His areas of interest are Digital Signal processing, Technology (IACSIT), Singapore. Her areas of interest are Image Image Processing and Computer Networking. He has more than 250 Processing, Signal Processing and Computer Graphics. She has 60 papers in National / International Conferences and Journals to his credit. papers in National /International Conferences/journal to her credit. He was Senior Member of IEEE. Presently He is Fellow of IETE and Life Member of ISTE Recently six students working under his guidance Kalpana R. Sagvekar has received B.E.(Computer) degree from have received best paper awards. Currently 10 research scholars are Mumbai University with first class in 2001. pursuing Ph.D. program under his guidance. Currently Perusing M.E. in Computer Engineering from University of Mumbai. She has more than 08 Ms. Archana A. Athawale has Received B.E.(Computer Engineering) years of experience in teaching. Currently degree from Walchand College of Engineering, working as Lecturer in Computer Engineering at Sangli, Shivaji University in 1996, Fr. Conceicao Rodrigues College of Engineering, M.E.(Computer Engineering) degree from Bandra(w), Mumbai. Her areas of interest are V.J.T.I., Mumbai University in 1999, currently Image Processing, Data Structure, Analysis of pursuing Ph.D. from NMIMS University, Algorithms, and Theoretical Computer Science. She has about 2 papers Mumbai. She has more than 10 years of in National /International Conferences/Journals to her credit. experience in teaching. Presently working as - an Assistant Professor in Department of Computer Engineering at Thadomal Shahani Engineering College, Mumbai. She is a Life member 95 http://sites.google.com/site/ijcsis/ ISSN 1947-5500