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Spatial steganalysis using redistributed residuals and diverse ensemble classifier

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Abstract

This paper proposes a spatial steganalytic scheme based on redistributed residuals and diverse ensemble classifier. In the scheme, the residuals obtained by local pixel predictors are shifted and suppressed to form the redistributed residuals, and then 2929 dimension features are calculated from the first-order statistic of original and redistributed residuals. With the aid of first-order statistic in a broad range, the feature can preserve the long-range dependencies between pixels, especially for highly adaptive steganography. Another advantage of the feature is that the dimension of first-order statistic is linear with range of residuals, so that the computational complexity is lowered. Moreover, with Bagging and AdaBoost mechanisms introduced here, we can get diverse representation of final feature and enhance the individual weak classifier, respectively. Compared with previous works, experimental results show that our scheme is effective at low embedding rate and is characterized by lower computational complexity.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grants 61472235, 61202367, 61272043 and 61373151, the Research Fund for the Doctoral Program of Higher Education of China under Grant 20113108110010, the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning, Shanghai Pujiang Program under Grant 13PJ1403200 and Shanghai Rising-Star Program under Grant 14QA1401900.

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Authors and Affiliations

  1. School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, China

    Jiang Yu & Xinpeng Zhang

  2. College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai, 200090, China

    Fengyong Li

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  1. Jiang Yu
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  2. Xinpeng Zhang
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  3. Fengyong Li
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Correspondence to Jiang Yu.

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Yu, J., Zhang, X. & Li, F. Spatial steganalysis using redistributed residuals and diverse ensemble classifier. Multimed Tools Appl 75, 13613–13625 (2016). https://doi.org/10.1007/s11042-015-2742-y

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  • DOI: https://doi.org/10.1007/s11042-015-2742-y

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