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A subspace learning-based method for JPEG mismatched steganalysis

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Abstract

The prevailing steganalysis detector trained by a source is used to recognize images from another different source, the detection accuracy typically drops owing to the mismatch between the two sources. In contrast to previous mismatched steganalysis methods, in this paper, we develop an unsupervised subspace learning-based method which has some differences from the ones common used in mismatched steganalysis. The proposed method employs low-rank and sparse constraints on the reconstruction coefficient matrix to maintain the global and local structures of the data. In this way, we can obtain new feature representations so that the feature distributions of the training and test data are close. We further promote the performance of the proposed method by employing the l2,1-norm on the error matrix. Comprehensive experiments on the JPEG mismatched steganalysis are conducted, and the experimental results show that the proposed method can improve the detection accuracy.

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Acknowledgments

The work is supported by the National Natural Science Foundation of China (Grant No. 61872368, No. 61802410, No. U1536121, No.11171346). The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.

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Authors and Affiliations

  1. College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China

    Yiming Xue, Liran Yang & Juan Wen

  2. School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Shaozhang Niu

  3. College of Science, China Agricultural University, Beijing, 100083, China

    Ping Zhong

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  1. Yiming Xue
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  2. Liran Yang
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  3. Juan Wen
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  4. Shaozhang Niu
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  5. Ping Zhong
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Correspondence to Ping Zhong.

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Xue, Y., Yang, L., Wen, J. et al. A subspace learning-based method for JPEG mismatched steganalysis. Multimed Tools Appl 78, 8151–8166 (2019). https://doi.org/10.1007/s11042-018-6719-5

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  • DOI: https://doi.org/10.1007/s11042-018-6719-5

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