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Blind JPEG steganalysis based on DCT coefficients differences

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Abstract

At recently, the most of the digital images are stored and transferred in their compressed format using discrete cosine transform (DCT)-based compression technique. DCT is one of the most important data compression technique due to the efforts from Joint Photographic Experts Group(JPEG). Blind steganalysis means how to detect the presence of the messages that are hidden using different types of steganography algorithms and has the ability to detect new unknown steganography algorithms. This paper presents blind steganalysis technique that can reliably detect the existence of messages hidden in JPEG files. In order to save the computation and memory cost, it is desirable to have image processing operations implemented directly in the DCT domain. The proposed method is based on extracting features directly from DCT domain through the analysis of differences between DCT coefficients before and after cropping. The extracted features are prepared as input to Support Vector Machine (SVM) to classify the image as stego (image that contain secret message) or clean (image that does not contain secret message). The experiments performed show that the proposed method yields better classification accuracy compared with other related works.

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References

  1. Bhme R, Westfeld A (2004) Breaking cauchy model-based jpeg steganography with first order statistics. Proceedings of 9th Eur. Symp. Research computer security (ESORICS). Sophia Antipolis, France 3193:125–140. doi:10.1007/978-3-540-30108-0_8

    Google Scholar 

  2. Cai K, Li X, Zeng T, Yang B, Lu X (2010) Reliable histogram features for detecting LSB matching. Image processing (ICIP), 2010 17th IEEE international conference on, pp 1761–1764. doi:10.1109/ICIP.2010.5651567

  3. Chang C-C, Lin C-J (2001) LIBSVM a library for support vector machines http://www.csie.ntu.edu.tw/~cjlin/libsvm. Accessed 1 Oct 2013

  4. Chen GM, Chen Q, Zhang D, Zhou DN (2014) Steganalysis based on distribution characters of stego-images in reduced dimension space. Multimedia Tools and Applications 71:497–515

    Article  Google Scholar 

  5. Chutani S, Goyal H (2012) Lsb embedding in spatial domain - a review of improved techniques. International Journal of Computers & Technology 3:153–157

    Google Scholar 

  6. European southern observatory (ESO) photo gallery. http://www.eso.org/public/images/. Accessed 1 Oct 2015

  7. Fridrich J (2005) Feature-based steganalysis for jpeg images and its implications for future design of steganographic schemes. Information Hiding, Springer 3200:67–81. doi:10.1007/978-3-540-30114-1_6

    Article  Google Scholar 

  8. Fridrich J, Goljan M, Du R (2001) Detecting LSB steganography in color, and gray-scale images. MultiMedia, IEEE 8:22–28. doi:10.1109/93.959097

    Article  Google Scholar 

  9. Fridrich J, Goljan M, Hogea D (2002) Steganalysis of jpeg images: breaking the f5 algorithm. Proceedings of 5th Int. Workshop Information Hiding 2578:310–323. doi:10.1007/3-540-36415-3_20

    Article  MATH  Google Scholar 

  10. Fridrich J, Goljan M, Hogea D, Soukal D (2003) Quantitative steganalysis of digital images: estimating the secret message length. Multimedia Systems, Springer 9:288–302. doi:10.1007/s00530-003-0100-9

    Article  Google Scholar 

  11. Fridrich J, Goljan M, Soukal D (2005) Perturbed quantization steganography. Multimedia Systems, Springer 11:98–107. doi:10.1007/s00530-005-0194-3

    Article  Google Scholar 

  12. Fridrich J, Pevn T, Kodovsk J (2007) Statistically undetectable jpeg steganography: dead ends, challenges, and opportunities. Proceedings of 9th ACM Multimedia & Security Workshop, pp 3–14

  13. Hamid N, Yahya A, Ahmad B, Al-qershi O (2012) Image steganography techniques: an overview. International Journal of Computer Science and Security (IJCSS) 6:168–187

    Google Scholar 

  14. Hetzl S, Mutzel P (2005) A graph-theoretic approach to steganography. Communications and Multimedia Security, Springer 3677:119–128. doi:10.1007/11552055_12

    Article  Google Scholar 

  15. Hsu C, Chang C, Lin C (2003) A practical guide to support vector classification http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf. Accessed 1 Oct 2013

  16. Huang Q, Ouyang W (2010) Protect fragile regions in steganography LSB embedding. In 3rd international symposium on knowledge acquisition and modeling, pp 175–178

  17. Jiang J, Feng G (2002) The spatial relationship of dct coefficients between a block and its sub-blocks. Signal Processing, IEEE Transactions on 50:1160–1169. doi:10.1109/78.995072

    Article  Google Scholar 

  18. Johnson NF, Katzenbeisser SC (2000) A survey of steganographic techniques. Information Hiding Techniques for Steganography and Digital Watermarking, pp:43–78

  19. A. Latham, Jphide& seek. http://linux01.gwdg.de/~alatham/stego.html

  20. Lerch-Hostalot D, Megas D (2013) LSB matching steganalysis based on patterns of pixel differences and random embedding. Computers & Security 32:192–206. doi:10.1016/j.cose.2012.11.005

    Article  Google Scholar 

  21. Li B, Shi Y, Huang JW (2009) Steganalysis of YASS. IEEE Transactions on Information Forensics and Security 4:369–382. doi:10.1109/TIFS.2009.2025841

    Article  Google Scholar 

  22. Li B, He J, Huang J, Shi YQ (2011) A survey on image steganography and steganalysis. Information Hiding and Multimedia Signal Processing 2:142–172

    Google Scholar 

  23. Liu Q, Sung A, Qiao M, Chen Z, Ribeiro B (2010) An improved approach to steganalysis of JPEG images. Inf Sci 180:1643–1655

    Article  Google Scholar 

  24. Luo W, Huang F, Huang J (2011) A more secure steganography based on adaptive pixelvalue differencing scheme. Multimedia Tools and Applications 52:407–430

    Article  Google Scholar 

  25. Meghanathan N, Nayak L (2010) Steganalysis algorithms for detecting the hidden information in image, audio and video cover media. International Journal of Network Security & Its Application 2:43–55

    Article  Google Scholar 

  26. Mielikainen J (2006) LSB matching revisited. IEEE Signal Process Lett 13:285–287

    Article  Google Scholar 

  27. National resource conservation system (NRCS) photo gallery. http://photogallery.nrcs.usda.gov. Accessed 1 Oct 2015

  28. Pevn T, Fridrich J (2005) Towards multi-class blind steganalyzer for jpeg images. Digital Watermarking, Springer 3710:39–53. doi:10.1007/11551492_4

    Article  Google Scholar 

  29. Sharma M, Bera S (2012) A review on blind still image steganalysis techniques using features extraction and pattern classification method. International Journal of Computer Science, Engineering and Information Technology 2:117–135. doi:10.5121/ijcseit.2012.2308

    Article  Google Scholar 

  30. Sharp T (2001) An implementation of key based digital signal steganography. Proceedings Information Hiding Workshop, Springer LNCS 2137:13–26. doi:10.1007/3-540-45496-9_2

    Article  MATH  Google Scholar 

  31. Wang K, Lu ZM, Hu YJ (2013) A high capacity lossless at hiding scheme for jpeg images. J Syst Softw 86:1965–1975

    Article  Google Scholar 

  32. Wang R, Xu M, Ping X, Zhang T (2015) Steganalysis of jpeg images by block texture based segmentation. Multimedia Tools and Applications 74:5725–5746

    Article  Google Scholar 

  33. Westfeld A (2001) F5-a steganographic algorithm (high capacity despite bet-tersteganalysis). Information Hiding, Springer 2137:289–302. doi:10.1007/3-540-45496-9_21

    Article  MATH  Google Scholar 

  34. Xi L, Ping X, Zhang T (2010) Improved LSB matching steganography resisting histogram attacks. IEEE, pp 203–206

  35. Zeng XT, Ping LD, Pan XZ (2010) A lossless robust data hiding scheme. Pattern Recogn 43:1656–1667

    Article  MATH  Google Scholar 

  36. Zhang J, Hu Y, Yuan Z (2009) Detection of LSB matching steganography using the envelope of histogram. Journal of Computers 4:646–653. doi:10.4304/jcp.4.7.646-653

    Google Scholar 

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Correspondence to Amr Magdy Rabee.

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Rabee, A.M., Mohamed, M.H. & Mahdy, Y.B. Blind JPEG steganalysis based on DCT coefficients differences. Multimed Tools Appl 77, 7763–7777 (2018). https://doi.org/10.1007/s11042-017-4676-z

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  • DOI: https://doi.org/10.1007/s11042-017-4676-z

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