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Mixing high-dimensional features for JPEG steganalysis with ensemble classifier

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Abstract

This paper proposes a JPEG steganalysis scheme based on the ensemble classifier and high-dimensional feature space. We first combine three current feature sets and remove the unimportant features according to the correlation between different features parts so as to form a new feature space used for steganalysis. This way, the dependencies among cover and steganographic images can be still represented by the features with a reduced dimensionality. Furthermore, we design a proportion mechanism to manage the feature selection in two subspaces for each base learner of the ensemble classifier. Experimental results show that the proposed scheme can effectively defeat the MB and nsF5 steganographic methods and its performance is better than that of existing steganalysis approaches.

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References

  1. Avcibas, I., Kharrazi, M., Memon, N.-D., Sankur, B.: Image steganalysis with binary similarity measures. EURASIP J. Appl. Signal Process. 17, 2749–2757 (2005)

    Article  Google Scholar 

  2. Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm (2001)

  3. Chen, C., Shi, Y.-Q.: JPEG image steganalysis utilizing both intrablock and interblock correlations. In: Proceedings IEEE International Symposium Circuits Syst., pp. 3029–3032, Seattle, Washington, May 18–21 (2008)

  4. Cortes, C., Vapnik, V.: Support vector networks. Mach. Learn. 20, 273–297 (1995)

    MATH  Google Scholar 

  5. Duda, R.-O., Hart, P.-E., Stork, D.-G.: Pattern Classification, 2nd edn. Wiley, New York (2001)

    MATH  Google Scholar 

  6. Farid, H., Siwei, L.: Detecting hidden messages using higher-order statistics and support vector machines. In: Proceedings 5th International Workshop Information Hiding. vol. 2578, pp. 340–354. LNCS, Noordwijkerhout, Netherlands, Oct. 7–9 (2002)

  7. Fillatre, L.: Adaptive steganalysis of least significant bit replacement in grayscale natural images. In: IEEE Trans. Signal Process. 60(2), 556–569 (2012)

    MathSciNet  Google Scholar 

  8. Fridrich, J.: Feature-based steganalysis for JPEG images and its implications for future design of steganographic schemes. In: Proceedings 6th International Workshop Information Hiding, LNCS vol. 3200, pp. 67–81. Toronto, ON, Canada (2004)

  9. Fridrich, J., Pevny, T., Kodovsky, J.: Statistically undetectable JPEG steganography: dead ends, challenges, and opportunities. In: Proceedings 9th ACM Multimedia Security, pp. 3–14. Dallas, TX, Sep. 20–21 (2007).

  10. Goljan, M., Fridrich, J., Holotyak, T.: New blind steganalysis and its implications. In: Proceedings SPIE Security and Watermarking of Multimedia Contents VIII, vol. 6072, pp. 1–13. San Jose, CA, Jan. 16–19 (2006).

  11. Kodovsky, J.: Ensemble classification in steganalysis-cross-validation and AdaBoost. Technical report (Aug. 2011)

  12. Kodovsk, J., Fridrich, J.: Calibration revisited. In: Proceedings 11th ACM Workshop Multimedia Security, pp. 63–74. Princeton, NJ, Sep. 7–8 (2009)

  13. Kodovsky, J., Fridrich, J.: Steganalysis in high dimensions: fusing classifiers built on random subspaces. In: Proceedings SPIE Electronic Imaging, Media, Watermarking, Security and Forensics XIII, vol. 7880, pp. 1–13, San Francisco, CA, Jan. 23–26 (2011)

  14. Kodovsky, J., Fridrich, J., Holub, V.: Ensemble classifiers for steganalysis of digital media. In: IEEE Trans. Inf. Forensics Secur. 7(2), 432–444 (2012)

    Google Scholar 

  15. Pevny, T., Fridrich, J.: Merging Markov and DCT features for multi-class JPEG steganalysis. In: Proceedings SPIE Security and Watermarking of Multimedia Contents IX, vol. 6505, pp. 3–4. San Jose, CA, Jan. 29-Feb. 1 (2007)

  16. Pevny, T., Bas, P., Fridrich, J.: Steganalysis by subtractive pixel adjacency matrix. In: IEEE Trans. Inf. Forensics Secur. 5(2), 215–224 (2010)

    Google Scholar 

  17. Quach, T.-T.: Optimal cover estimation methods and steganographic payload location. In: IEEE Trans. Inf. Forensics Secur. 6(4), 1214–1222 (2011)

    Google Scholar 

  18. Sallee, P.: Model-based steganography. In: Proceedings 2th International Workshop Digital Watermarking, vol. 2939, pp. 154–167. LNCS, Seoul, Korea, Oct. 30–Nov. 1 (2004)

  19. Shi, Y.-Q., Chen, C., Chen, W.: A Markov process based approach to effective attacking JPEG steganography. In: Proceedings 8th International Workshop on Information Hiding, LNCS vol. 4437, pp. 249–264. Alexandria, VA, July 10–12 (2007)

  20. Software available at http://www.agents.cz/boss/BOSS-Final/index.php?mode=VIEW&tmpl=materials

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Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grants (61073190), and the Ph.D. Programs Foundation of Ministry of Education of China (20103108120011 and 20113108110010).

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Authors and Affiliations

  1. School of Communication and Information Engineering, Shanghai University, Shanghai, 200072 , People’s Republic of China

    Bin Chen, Guorui Feng, Xinpeng Zhang & Fengyong Li

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  1. Bin Chen
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  2. Guorui Feng
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  3. Xinpeng Zhang
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  4. Fengyong Li
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Correspondence to Guorui Feng.

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Chen, B., Feng, G., Zhang, X. et al. Mixing high-dimensional features for JPEG steganalysis with ensemble classifier. SIViP 8, 1475–1482 (2014). https://doi.org/10.1007/s11760-012-0380-7

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  • DOI: https://doi.org/10.1007/s11760-012-0380-7

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