YET Another Steganographic Scheme (YASS) [1] is a

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1 374 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 5, NO. 3, SEPTEMBER 2010 An Experimental Study on the Security Performance of YASS Fangjun Huang, Member, IEEE, Jiwu Huang, Senior Member, IEEE, and Yun Qing Shi, Fellow, IEEE Abstract This paper presents an experimental study on the security performance of Yet Another Steganographic Scheme (YASS). It reports: 1) YASS s security performance with different input images, i.e., uncompressed images and JPEG compressed images; 2) YASS s security performance compared with two other JPEG steganographic schemes MB1 and F5; and 3) some experimental results about extended YASS. Index Terms Security performance, steganographic, Yet Another Steganographic Scheme (YASS). I. INTRODUCTION YET Another Steganographic Scheme (YASS) [1] is a newly developed JPEG steganographic scheme. Through using a randomized embedding strategy, it has been reported that YASS can effectively disable some major blind steganalyzers [2] [6] to a great extent. The stego image generated by YASS is in JPEG format, which is the most commonly used image format today. However, different from other well-known JPEG steganographic schemes such as Outguess [7], F5 [8], and MB1 [9], YASS does not embed a secret message into the quantized block discrete cosine transform (BDCT) coefficients of the JPEG image directly. The main procedure of YASS includes the following four steps. 1) Divide a given image into consecutive and nonoverlapping blocks with the size, called big block. In this step, the BMP, TIFF, and JPEG format images all can be chosen to use. However, if the given image s format is JPEG, it should be decompressed first. 2) In each big block, randomly select 8 8 block(s) which may not coincide with the 8 8 grid used in standard JPEG compression, and then apply BDCT to the selected Manuscript received February 01, 2010; revised May 04, 2010; accepted June 14, Date of publication June 28, 2010; date of current version August 13, This work was supported by 973 Program of China (Grant 2006CB303104), by the National Natural Science Foundation of China (Grant ), by the Fundamental Research Funds for the Central Universities, and by the Research Fund for the Doctoral Program of Higher Education of China (Grant ). The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Nasir Memon. F. Huang was with the Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ USA. He is now with the School of Information Science and Technology, Sun Yat-Sen University, Guangzhou, GD , China ( huangfj@mail.sysu.edu.cn). J. Huang is with the School of Information Science and Technology, Sun Yat-Sen University, Guangzhou, GD , China ( isshjw@mail.sysu. edu.cn). Y. Q. Shi is with the Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ USA ( shi@njit. edu). Color versions of one or more of the figures in this paper are available online at Digital Object Identifier /TIFS block(s). The repeat-accumulate (RA) coded secret message bits are embedded into a group of predetermined low-frequency BDCT coefficients at a design quality factor QF using the quantization index modulation (QIM) strategy. 3) After data embedding, multiply the 8 8 BDCT coefficients in which the RA coded bits were embedded by the quantization matrix corresponding to the design quality factor QF and perform the inverse 2-D DCT. Then the obtained spatial domain 8 8 block is placed back to its original location in the given image. 4) Compress the obtained image from Step 3 with an advertised image quality factor QF, resulting in a JPEG stego image. Since it was published, YASS has received much attention. Kodovský and Fridrich [10] pointed out that despite the fact that the embedding distortion of YASS is quite large, the standard JPEG compression in the fourth step could conceal the direct embedding effect in the second step to a great extent, and the embedding distortion could not be explored easily from the advertised stego image. Thus, YASS still can make many blind steganalyzers unable to detect the stego contents. In [10], the authors also reported that when the input is an uncompressed image, YASS would be particularly difficult for the steganalyzer [6] to detect. In [11], Li et al. pointed out that the positions of 8 8 blocks selected for embedding in Step 2 of YASS s embedding procedure are not randomized enough, and hence the possible locations and definitely impossible locations of embedding blocks could be identified. Consequently, the fact that extra zero coefficients have been introduced into the selected blocks by the QIM embedding can be exploited, and a specific steganalyzer is designed to detect YASS effectively. Recently, some more research about YASS and its extended versions [12] has been conducted. In [13], Kodovský and Fridrich revisited the calibration technique. Based on the improved understanding of the process of calibration, they explained why calibrated feature sets failed to detect YASS and its extended versions. In [14], Kodovský et al. pointed out that modern blind steganalysis tools developed for detection of JPEG and spatial-domain steganography were capable of detecting YASS and its extended versions, and a new steganalytic scheme was designed for detecting YASS and its extended versions. In this paper, an experimental study on the security performance of YASS is presented. Our study is different from the previous works [13], [14] in the following two respects. First, the security performance of YASS with different types of input images, i.e., uncompressed and JPEG images, is detailedly studied through using four state-of-the-art blind JPEG steganalyzers. Second, the security performance of YASS is evaluated through comparing it with that of MB1 and F5. In doing so, for each /$ IEEE

2 HUANG et al.: EXPERIMENTAL STUDY ON THE SECURITY PERFORMANCE OF YASS 375 image among a large-scale image dataset, the number of information bits embedded by MB1 and F5 is the same as that embedded by YASS. In this paper, we mainly focus on the security performance of YASS [1]. However, some experimental results about extended YASS, denoted by EYASS, are also given for demonstration. The rest of this paper is organized as follows. In Section II, the security performance of YASS with different types of input images is addressed. In Section III, the security performance of YASS is evaluated through comparing it with that of MB1 and F5. Some experimental results about EYASS are given in Section IV. Lastly, our conclusion is drawn in Section V. II. SECURITY PERFORMANCE OF YASS In this section, we report the security performance of YASS against four state-of-the-art blind JPEG steganalyzers. The first three steganalyzers are denoted by MP-324 [15], MP-486 [16], and JFMP-274 [6], respectively, where the numbers 324, 486, and 274 denote the total number of features utilized for steganalysis, and MP stands for the Markov process and JF for JPEG features. The first two schemes are Markov-process-based steganalytic schemes and do not rely on the self-calibration technique [5], and the third one is a self-calibration-based JPEG steganalyzer. Please note that these three steganalyzers mainly extract features from the BDCT domain, and are effective in analyzing the JPEG steganographic schemes such as Outguess, F5, and MB1. Besides these three steganalyzers, another JPEG steganalyzer, denoted by NoClbJFMP-274 in this paper, is also adopted in our study to demonstrate the desynchronization capability of YASS. As we know, all the features of JFMP-274 are calibrated features, which are calculated as the difference between that extracted from the test image and that extracted from its corresponding calibrated image. For NoClbJFMP-274, all of the 274 features are extracted from the test image directly without calibration. Through comparing the detection results of this pair (i.e., JFMP-274 and NoClbJFMP-274) of steganalyzers, the desynchronization capability of YASS will be demonstrated more clearly. Our test image set consists of 5000 uncompressed images. Among them, 2631 images were taken by members of our group in different scenarios with different cameras, 1543 images were downloaded from NRCS [20], and the remaining 1096 images are from CorelDraw image dataset [21]. All 5000 images are central-cropped into the size of The YASS code was downloaded from [1] in which the 19 lowest frequency BDCT coefficients were chosen as the data embedding band. Different big block sizes and JPEG quality factors are tested in our experiments. The big blocks with the size 9 9, 10 10, 12 12, and are tested and denoted by,,, and, respectively. There are two types of input images for YASS. We name them Cover-I and Cover-II, respectively. Cover-I represents the uncompressed images such as BMP, TIFF, etc., and Cover-II is the JPEG compressed images which are obtained from Cover-I images via applying standard JPEG compression. Since the RA codes are required by YASS, we have considered the redundancy factor of the RA codes in all our experiments. According to [17] [19], the minimum coding redundancy factor of RA codes for each of 5000 images has been searched out through implementing the RA codes repeatedly. The binary search strategy is adopted by us to improve the search speed. Suppose that the image size is. For each image, the number of information bits embedded by YASS is computed as, where represents the big block size. The (bits per nonzero BDCT AC coefficients) value for each image is computed in our experiments. When computing the values, the nonzero BDCT AC coefficients are computed from the Cover-II images directly. It is noted that the value represents the amount of information bits that YASS has embedded. In our experiments, the training set for every classifier contained 4000 Cover-II images and 4000 stego images which are randomly selected. The remaining 1000 Cover-II and 1000 stego images are used for testing. The TNR and TPR represent the true negative rate and true positive rate, respectively, and AR AR TNR TPR represents the accuracy rate. The support vector machine (SVM) is adopted as the classifier and the code is obtained from LibSVM [22]. In all our experiments, the second-order polynomial kernel is used. A. Test on Cover-I Images Here, the security performance of YASS with Cover-I input images is reported. That is, all the input images of YASS are uncompressed images. In Table I, the average values of and the detection results of YASS with JFMP-274, MP-324, MP-486, and NoClbJFMP-274 are listed. It is observed from Table I that all of these four steganalyzers can detect YASS better than random guessing under almost all test conditions, except if JFMP-274 is selected for the big block case of and QF QF. This means that the distortion introduced by YASS can be explored by these four JPEG steganalyzers. Note that, YASS has significantly lowered the steganalytic capability of the JFMP-274 scheme, which uses self-calibration and is very efficient in attacking other JPEG steganographic schemes such as Outguess, F5, and MB1. In Table I, the shaded data in italics represents the best detection accuracy rates that can be achieved by these four steganalyzers for each case. We can find that the steganalytic schemes such as MP-324, MP-486, and NoClbJFMP-274 without using the self-calibration technique can detect YASS better than JFMP-274. Furthermore, comparing the detection results of JFMP-274 and No- ClbJFMP-274, we can find that the detection accuracy rates achieved by NoClbJFMP-274 are constantly higher than those by JFMP-274. Namely, the noncalibrated features are more efficient in detecting YASS than the calibrated features as the uncompressed image is used as the input medium of YASS. It is then concluded that the embedding strategy of YASS is efficient in invalidating the self-calibration technique, and some JPEG steganalyzers without using self-calibration can detect YASS more effectively. B. Test on Cover-II Images In this section, we investigate the security performance of YASS when the Cover-II image is used as the input medium. According to YASS s embedding procedure, if a JPEG image is used as the input medium, it should be decompressed first, and then randomly select 8 8 blocks which may not coincide with the 8 8 grid in standard JPEG compression for data hiding. Obviously, a difference does exist between the decompressed

3 376 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 5, NO. 3, SEPTEMBER 2010 TABLE I DETECTION RATES AGAINST YASS WITH DIFFERENT STEGANALYZERS. (STEGO IMAGES ARE GENERATED FROM UNCOMPRESSED INPUT IMAGES. THE SHADED DATA IN ITALICS REPRESENT THE BEST DETECTION ACCURACY RATES THAT CAN BE ACHIEVED BY THE FOUR JPEG STEGANALYZERS. TNR, TPR, AND AR DENOTE THE TRUE NEGATIVE RATE, TRUE POSITIVE RATE, AND ACCURACY RATE, RESPECTIVELY) TABLE II DETECTION RATES AGAINST YASS WITH DIFFERENT STEGANALYZERS. (STEGO IMAGES ARE GENERATED FROM JPEG INPUT IMAGES. THE SHADED DATA IN ITALICS REPRESENT THE BEST DETECTION ACCURACY RATES THAT CAN BE ACHIEVED BY THE FOUR JPEG STEGANALYZERS) JPEG image and its corresponding original uncompressed images, and this difference may result in a different security performance of YASS. In our experiments, the input Cover-II image has the same quality factor as the advertised stego image. For example, in the case, the input JPEG images are with the quality factor 50, and in the and cases, the input JPEG images are with the quality factor 75. Note that the numbers at the arrow tail represent the design quality factor QF and the numbers at the arrow head represent the advertised quality factor QF, respectively. The average values of and detection results of JFMP-274, MP-324, MP-486, and NoClb- JFMP-274 are shown in Table II. It is observed that in some cases there is a large difference between the average values in Tables I and II, for example, in the case that the big block size is chosen as 9 or 10 and QF QF. Comparing Table II with Table I, we can find that in 7 out of 12 cases, the uncompressed input image and JPEG input image may have similar embedding rates (the difference is less than 0.01 ). It is expected that with the similar embedding rates, similar detection accuracy rates should be achieved with the aforementioned four steganalytic schemes. In fact, this is true for MP-324, MP-486, and NoClbJFMP-274. For JFMP-274, however, in the cases with the similar embedding rates, the detection accuracy rates in Table II are much higher than those reported in Table I. For example, in 5 out of 7 cases with similar embedding rates, the detection accuracy rates of JFMP-274 in Table II are higher than those in Table I by at least 10%. But for those noncalibration-based JPEG steganalyzers, in all cases with similar embedding rates, no detection rate in Table II is higher than the corresponding one in Table I beyond 10%. From Table II, we can also find that in most cases, JFMP-274 has the highest detection rate among the aforementioned four steganalyzers. In Table II, the shaded data in italics represents the best detection accuracy rates that can be achieved by these four steganalyzers. Furthermore, comparing detection results of JFMP-274 and NoClbJFMP-274, we can find that in 8 out of 12 cases, JFMP-274 has better detection performance than NoClbJFMP-274. Based on these observations, we can conclude that if the JPEG image is used as the inputted medium, the desynchronization capability of YASS will decrease, and the

4 HUANG et al.: EXPERIMENTAL STUDY ON THE SECURITY PERFORMANCE OF YASS 377 TABLE III DETECTION RATES AGAINST MB1 AND F5 WHICH HAVE THE SAME EMBEDDING RATE AS THAT OF YASS WITH UNCOMPRESSED INPUT IMAGES. (THE UNDERLINED DATA REPRESENT THAT UNDER THIS CONDITION, MB1 OR F5 HAS BETTER SECURITY PERFORMANCE THAN THAT OF YASS) self-calibration technique will be valid again. Another observation that can be made from comparing Tables II and I is that in almost all cases with similar embedding rates, the detection accuracy rates (achieved by any one of the aforementioned four steganalyzers) in Table II are higher than the corresponding ones in Table I, which means that more distortion will be easier to leave when a JPEG image is used as the input medium of YASS. In addition, it is observed from Tables I and II that no matter what kind of image is adopted as the input medium of YASS, the embedding rate corresponding to big block size is larger than that corresponding to big block size. However, in almost all cases, the security performance of YASS with big block size is higher than that with big block size. This unusual phenomenon mainly comes from YASS s randomized embedding strategy. III. COMPARING THE SECURITY PERFORMANCE OF YASS WITH THAT OF MB1 AND F5 In this section, we further study YASS through comparing its security performance with MB1 and F5. Different from YASS, MB1 and F5 embed information bits into a JPEG image via modifying its quantized BDCT coefficients. If an input image is in BMP or TIFF format, it should be compressed first to a JPEG image, and then the quantized BDCT coefficients are modified to hide information bits. Thus, in all of the following experiments, the input images of MB1 and F5 are in JPEG format. It is noted that for MB1 and F5, the quality factor of the image before and after embedding is equal to the advertised quality factor of YASS, i.e., QF in our experiments. Four blind steganalyzers discussed in Section II, i.e., JFMP- 274, MP-324, MP-486, and NoClbJFMP-274, are also used in this experiment. The detection results are shown in Tables III and IV. In Table III, we have compared the security performance of YASS with that of MB1 and F5 if the uncompressed image is chosen as the input medium of YASS, and in Table IV, we have compared the security performance of YASS with that of MB1 and F5 if the JPEG image is chosen as the input medium of YASS. It is noted that for each image, the same number of information bits is embedded into the cover image by MB1 and F5 as that embedded by YASS in all cases (the average values are given in Tables III and IV). For example, in Table III, the number of information bits that has been embedded by MB1 and F5 is equal to the number that has been embedded by YASS with uncompressed input image. In Table IV, the number of information bits that has been embedded by MB1 and F5 is equal to the number that has been embedded by YASS with the JPEG input image. In Tables III and IV, the underlined detection rates represent that, in this case, YASS is less secure than MB1 or F5. Please note that the detection results in Table III are compared with those in Table I, and the detection results in Table IV are compared with those in Table II, respectively. It is observed from Table III that when an uncompressed image is used as the input medium of YASS, in most cases YASS has better security performance than MB1 when the aforementioned four steganalyzers are selected as the detector. Compared with F5, YASS will have a better security performance when JFMP-274 is selected as the detector. Whereas when noncalibration-based JPEG steganalyzers such as MP-324, MP-486, and NoClbJFMP-274 are selected as the detectors, YASS will be less secure than F5 in most cases. These results have also demonstrated the desynchronization capability of YASS with an uncompressed input image. When a JPEG image is used as the input medium of YASS, it is observed from Table IV that in general YASS is more secure than MB1 and less secure than F5 while the aforementioned four steganalyzers are selected as the detectors. From the comparison results listed in Tables III and IV, we can find that different steganalyzers may lead to different conclusions about the security performance of YASS. For example, if JFMP-274 is chosen as the detector, we can make a conclusion that YASS is more secure than F5 in general when an uncompressed image is used as the input medium of YASS. However, if other steganalyzers such as MP-324, MP-486, and No- ClbJFMP-274 are selected as the detector, our conclusion is that YASS is less secure than F5. Therefore, another point of view to compare the security performance of YASS, MB1, and F5 is to use the highest detection accuracy rates that can be achieved by the aforementioned four steganalyzers. In the following, the

5 378 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 5, NO. 3, SEPTEMBER 2010 TABLE IV DETECTION RATES AGAINST MB1 AND F5 WHICH HAVE THE SAME EMBEDDING RATE AS THAT OF YASS WITH JPEG INPUT IMAGES (THE UNDERLINED DATA REPRESENTS THAT UNDER THIS CONDITION, MB1 OR F5 HAS BETTER SECURITY PERFORMANCE THAN THAT OF YASS) Fig. 1. Comparison of the best detection accuracy rates against YASS, MB1, and F5. (a) For each image, the number of information bits embedded by MB1 and F5 is the same as that embedded by YASS with uncompressed input image. (b) For each image, the number of information bits embedded by MB1 and F5 is the same as that embedded by YASS with JPEG input image. best detection accuracy rates (achieved by the aforementioned four steganalyzers) against MB1, F5, and YASS in each case are selected for comparison. The comparison results are shown in Fig. 1, where the horizontal axis represents the values and the vertical axis represents the detection accuracy rates. It is observed from Fig. 1(a) that with an uncompressed input image, generally YASS will have a better security performance than MB1. In most cases, the detection accuracy rates of YASS and F5 are rather close, which means that they have similar security performance. From Fig. 1(b), we can find that with a JPEG input image, YASS will be more difficult to be detected than MB1 and easier to be detected than F5 in general. IV. EXPERIMENTS OF EXTENDED YASS (EYASS) In [12], some extended versions of YASS have been presented. The two main improvements are: 1) dynamically changing the design quality factor QF while embedding the RA coded bits; 2) iteratively embedding the message bits to resisting the JPEG attack in Step 4 of YASS s embedding procedure. The EYASS may have various combinations of parameters. In our experiments, the parameters are selected as: 1) the QF values of 70, 60, and 50 are used, respectively, for the blocks with variance values in the zones [0, 1), [1, 4), and [4, ) as in [12]; 2) the big block sizes are selected as 9, 10, 12, 14, respectively, as in our pervious experiments about original YASS. (Up to here, there are four different combinations of parameters.) Then conducting EYASS with and without iteratively embedding strategy will result in eight extended versions, which are studied in our work. For simplicity, the input images of EYASS are in BMP format, and only two steganalyzers, i.e., JFMP-274 and NoClbJFMP-274 are selected in our following experiments. The average values of embedding rates that can be achieved by EYASS and the corresponding detection accuracy rates against JFMP-274 and NoClbJFMP-274 are shown in Table V. For comparison, the detection rates of F5 and another more secure JPEG steganography MM3 [23] are also listed in the

6 HUANG et al.: EXPERIMENTAL STUDY ON THE SECURITY PERFORMANCE OF YASS 379 TABLE V COMPARISON OF EYASS, F5 AND MM3 (QF VALUES OF 70, 60 AND 50 ARE USED RESPECTIVELY FOR THE BLOCKS WITH VARIANCE IN THE ZONES [0, 1), [1, 4) AND [4, 1), REP AND NOREP REPRESENT EYASS WITH AND WITHOUT ITERATIVELY EMBEDDING STRATEGY, AND CORR. IS THE ABBREVIATION OF CORRESPONDING) table, respectively. As before, the same number of information bits has been embedded by EYASS, F5, and MM3 for each image. It is observed that some similar properties are possessed by YASS and EYASS: 1) with uncompressed input images, EYASS also possesses the desynchronization capability, e.g., with a relatively high embedding rate, EYASS may even have better security performance than MM3 when JFMP-274 is selected as the detector (note that MM3 is among the most secure JPEG steganographic schemes [24]); 2) the embedding rate corresponding to big block size is larger than that corresponding to big block size, whereas the security performance with big block size is higher than that with big block size. In addition, it is observed from comparing Table V with Table I that in some cases, an obvious improvement can be obtained by EYASS. For example, when the big block size is 10, the extended version with iteratively embedding strategy can achieve a higher embedding rate than the original YASS at the case of QF and QF, whereas lower detection accuracy rates against JFMP-274 and NoClbJFMP-274 have been obtained. In Fig. 2, the best detection accuracy rates (achieved by JFMP-274 and NoClbJFMP-274) against EYASS, F5, and MM3 are demonstrated, where the horizontal axis represents the values and the vertical axis represents the detection accuracy rate. Since EYASS has many possible parameter combinations, it is not easy to evaluate the security performance of EYASS according to our limited experiments. However, some observations can be made from Fig. 2. First, different from F5 and MM3, the security performance of EYASS does not monotonically decrease as the embedding rate increases. (Note that YASS does also have this nonmonotonicity property. However, since the stego images generated by YASS, MB1, and F5 in our previous experiments have two different quality factors, i.e., 50 and 75, the nonmonotonicity of YASS cannot be observed clearly from Fig. 1). Second, when the embedding rate is low, EYASS will be easier to be detected than F5 and MM3. As the embedding rate increases, the detection accuracy rates of these three different steganographic schemes will become closer. V. CONCLUSION We have studied the security performance of YASS in this paper. The following conclusions can be drawn from our extensive experiments. Fig. 2. Comparison of the detection accuracy rates of EYASS, F5, and MM3. (For each image, the number of information bits embedded by F5 and MM3 is the same as that embedded by EYASS with BMP input image.) 1) YASS s randomized embedding strategy opens a new direction to improve the security performance of steganographic schemes. This new randomized embedding strategy can effectively invalidate the self-calibration technique to a great extent when an uncompressed image is used as the input medium. Consequently, the blind JPEG steganalyzer which relies on the self-calibration technique will detect YASS less effectively than the noncalibration-based steganalyzers. 2) When a JPEG image is used as the input medium of YASS, the desynchronization capability of YASS will decrease drastically. Consequently, the self-calibration technique will be valid again, and the self-calibration-based blind JPEG steganalyzer will detect YASS more effectively than noncalibration-based JPEG steganalyzers in general. 3) Our experimental results have demonstrated that in most cases, with an uncompressed input image, YASS is more secure than MB1 and has a similar security performance as F5, whereas with JPEG input image YASS is easier to detect than F5 and still more difficult to detect than MB1. 4) Compared with YASS, some improvements can be made by EYASS. Our experimental results also demonstrate that similar properties are possessed by YASS and EYASS, such as the desynchronization capability, being sensitive to the big block size. ACKNOWLEDGMENT The authors acknowledge the technical discussion with Prof. J. Fridrich, Dr. B. Li, and Dr. C. Chen, and the kind offer of the codes of YASS algorithm by their authors. The authors also would like to thank the reviewers for their constructive comments. REFERENCES [1] K. Solanki, A. Sarkar, and B. S. Manjunath, YASS: Yet another steganographic scheme that resists blind steganalysis, in Proc. 9th Int. Workshop Information Hiding, 2007, pp [Online]. Available: LNCS 4567

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Circuits and Systems, Seattle, WA, May 18 21, [17] K. Solanki, N. Jacobsen, U. Madhow, B. S. Manjunath, and S. Chandrasekaran, Robust image-adaptive data hiding using erasure and error correction, IEEE Trans. Image Process., vol. 13, no. 12, pp , Dec [18] F. R. Kschischang, B. J. Frey, and H.-A. Loeliger, Factor graphs and the sum-product algorithm, IEEE Trans. Inf. Theory, vol. 47, no. 2, pp , Feb [19] A. Sarkar, L. Nataraj, B. S. Manjunath, and U. Madhow, Estimation of optimum coding redundancy and frequency domain analysis of attacks for YASS A randomized block based hiding scheme, in Proc. IEEE Int. Conf. Image Processing, San Diego, CA, Oct , [20] NRCS Photo Gallery [Online]. Available: usda.gov [21] CorelDraw Image CD [Online]. Available: [22] C.-C. Chang and C.-J. Lin, LIBSVM: A Library for Support Vector Machines 2001 [Online]. Available: libsvm [23] Y. Kim, Z. Duric, and D. Richards, Modified matrix encoding technique for minimal distortion steganography, in Proc. 8th Int. Workshop Information Hiding, 2007, pp , LNCS [24] J. Fridrich, T. Pevny, and J. Kodovsky, Statistically undetectable JPEG steganography: Dead ends, challenges, and opportunities, in Proc. ACM Workshop on Multimedia and Security, Dallas, TX, Sep , 2007, pp Fangjun Huang (M 07) received the B.S. degree from Nanjing University of Science and Technology, China, in 1995, and the M.S. and Ph.D. degrees from Huazhong University of Science and Technology, China, in 2002 and 2005, respectively. He is currently a faculty member with the School of Information Science and Technology, Sun Yat-Sen University, China. From June of 2009 to June of 2010, he was a postdoctoral researcher in the Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ. His research interests include digital forensics and multimedia security. Jiwu Huang (M 98 SM 00) received the B.S. degree from Xidian University, China, in 1982, the M.S. degree from Tsinghua University, China, in 1987, and the Ph.D. degree from the Institute of Automation, Chinese Academy of Science, in He is currently a Professor with the School of Information Science and Technology, Sun Yat-Sen University, Guangzhou, China. His current research interests include multimedia forensics and security. Dr. Huang has served as a Technical Program Committee member for many international conferences. He serves as a member of IEEE CAS Society Technical Committee of Multimedia Systems and Applications and the chair of IEEE CAS Society Guangzhou chapter. He is an Associate Editor of the EURASIP Journal of Information Security. Yun Qing Shi (M 90 SM 93 F 05) received the B.S. and M.S. degrees from Shanghai Jiao Tong University, Shanghai, China, and the M.S. and Ph.D. degrees from the University of Pittsburgh, PA. He joined the Department of Electrical and Computer Engineering at New Jersey Institute of Technology, Newark, NJ, in 1987, and is now a Professor there. His research interests include digital multimedia data hiding, steganaysis, forensics and information assurance, visual signal processing and communications, motion analysis, theory of multidimensional systems, and signal processing. He is an author/coauthor of more than 200 papers, a book, and five book chapters. He holds nine awarded U.S. patents and has additional 20 U.S. patents pending. Dr. Shi is the Editor-in-Chief of LNCS Transactions on Data Hiding and Multimedia Security (Springer), an Associate Editor of the Journal on Multidimensional Systems and Signal Processing (Springer). He served as an Associate Editor of IEEE TRANSACTIONS ON SIGNAL PROCESSING ( ) and IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II ( ), a member of editorial board of the International Journal of Image and Graphics ( ), and a guest editor of special issues for a few journals, an IEEE CASS Distinguished Lecturer ( ), the technical program chair of ICME07, cotechnical chair of IWDW06, 07, 09, MMSP05, and cogeneral chair of MMSP02. He is a member of a few IEEE technical committees, and a Fellow of IEEE for his contribution to Multidimensional Signal Processing.