KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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An Adaptive JPEG Steganographic Method Based on Weight Distribution for Embedding Costs

  • Sun, Yi (Zhengzhou Information Science and Technology Institute) ;
  • Tang, Guangming (Department of Information Security Zhengzhou Information Science and Technology Institute) ;
  • Bian, Yuan (Zhengzhou Information Science and Technology Institute) ;
  • Xu, Xiaoyu (Zhengzhou Information Science and Technology Institute)
  • Received : 2016.07.04
  • Accepted : 2017.02.10
  • Published : 2017.05.31
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Abstract

Steganographic schemes which are based on minimizing an additive distortion function defined the overall impacts after embedding as the sum of embedding costs for individual image element. However, mutual impacts during embedding are often ignored. In this paper, an adaptive JPEG steganographic method based on weight distribution for embedding costs is proposed. The method takes mutual impacts during embedding in consideration. Firstly, an analysis is made about the factors that affect embedding fluctuations among JPEG coefficients. Then the Distortion Update Strategy (DUS) of updating the distortion costs is proposed, enabling to dynamically update the embedding costs group by group. At last, a kind of adaptive JPEG steganographic algorithm is designed combining with the update strategy and well-known additive distortion function. The experimental result illustrates that the proposed algorithm gains a superior performance in the fight against the current state-of-the-art steganalyzers with high-dimensional features.

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References

  1. Sedighi V, Cogranne R and Fridrich J, "Content-Adaptive Steganography by Minimizing Statistical Detectability," IEEE Transactions on Information Forensics and Security, vol.11, no.2, pp. 221-234, 2016. https://doi.org/10.1109/TIFS.2015.2486744
  2. Song X, Liu F, Yang C, et al, "Steganalysis of adaptive JPEG steganography by selecting DCT coefficients according to embedding distortion," KSII Transactions on Internet And Information Systems, vol.9, no.12, pp. 5209-5228, 2015. https://doi.org/10.3837/tiis.2015.12.026
  3. Filler T, Judas J, and Fridrich J, "Minimizing additive distortion in steganography using syndrome-trellis codes," IEEE Transactions on Information Forensics and Security, vol.6, no.3, pp. 920- 935, 2011. https://doi.org/10.1109/TIFS.2011.2134094
  4. Holub V and Fridrich J, "Designing steganographic distortion using directional filters," in Proc. of the IEEE Workshop on Information Forensic and Security, Tenerife, Spain, pp.234-239, 2012.
  5. Holub V and Fridrich J, "Digital image steganography using universal distortion," in Proc. of 1st ACM Workshop on Information Hiding and Multimedia Security, Montpellier, France, pp.59-68, 2013.
  6. B. Li, M. Wang, J. Huang, and X. Li, "A new cost function for spatial image steganography," in Proc. of the IEEE International Conference on Image Processing, Pairs, France, pp.4206-4210, 2014.
  7. Sedighi V, Fridrich J and Cogranne R, "Content-adaptive pentary steganography using the multivariate generalized Gaussian cover model," in Proc. of IS&T/SPIE Electronic Imaging. International Society for Optics and Photonics, pp.94090H-94090H, March, 2015.
  8. Guo L, Ni J and Shi Y Q, "Uniform embedding for efficient JPEG steganography," IEEE Transactions on Information Forensics and Security, vol.9, no.5, pp. 814-825, 2014. https://doi.org/10.1109/TIFS.2014.2312817
  9. Guo L, Ni J, Su W, et al, "Using Statistical Image Model for JPEG Steganography: Uniform Embedding Revisited," IEEE Transactions on Information Forensics and Security, vol.10, no.12, pp. 2669-2680, 2015. https://doi.org/10.1109/TIFS.2015.2473815
  10. Filler T and Fridrich J, "Gibbs construction in steganography," IEEE Transactions on Information Forensics and Security, vol.5, no.4, pp. 705-720, 2010. https://doi.org/10.1109/TIFS.2010.2077629
  11. B. Li, M. Wang, J. Huang, and X. Li, "A Strategy of Clustering Modification Directions in Spatial Image Steganography," IEEE Transactions on Information Forensics and Security, vol.10, no.9, pp. 1905-1917, 2015. https://doi.org/10.1109/TIFS.2015.2434600
  12. Tang W, Li B, Luo W, et al, "Clustering Steganographic Modification Directions for Color Components," IEEE Signal Processing Letters, vol.23, no.2, pp. 197-201, 2016. https://doi.org/10.1109/LSP.2015.2504583
  13. Kodovský J and Fridrich J, "Steganalysis of JPEG images using rich models," IS&T/SPIE Electronic Imaging, International Society for Optics and Photonics, pp.83030A-83030A, February, 2012.
  14. Holub V and Fridrich J, "Low-complexity features for JPEG steganalysis using undecimated DCT," IEEE Transactions on Information Forensics and Security, vol.10, no.2, pp. 219-228, 2015. https://doi.org/10.1109/TIFS.2014.2364918
  15. Bas P, Filler T and Pevny T, "Break Our Steganographic System: The Ins and Outs of Organizing BOSS," in Proc. of International Workshop on Information Hiding, Springer Berlin Heidelberg, pp.59-70, May, 2011.
  16. Fridrich J, Pevny T and Kodovsky J, "Statistically undetectable jpeg steganography: dead ends challenges, and opportunities," in Proc. of the 9th workshop on Multimedia & security, ACM, pp.3-14, September, 2007.
  17. Kodovsky J, Fridrich J, and Holub V, "Ensemble classifiers for steganalysis of digital media," IEEE Transactions on Information Forensics and Security, vol.7, no.2, pp. 432-444, 2012. https://doi.org/10.1109/TIFS.2011.2175919