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Binary Image Steganalysis Based on Local Residual Patterns

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Security with Intelligent Computing and Big-data Services (SICBS 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 895))

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Abstract

A binary image steganalysis scheme based on the statistic model of local residual pattern (LRP) is proposed in this paper. LRP means the pattern of a local area of the residual map of the binary image, which is calculated with the XOR operation. The XOR operation is sensitive to the difference between adjacent pixels, which leads to the emphasis on edge property of the residual map. The neighbouring LRPs of the modified pixel will be affect, which makes the statistic model of LRPs change. Thus the trace of steganography can be detected according to the difference between the statistic models. Finally, the experiments we conducted show that our proposed scheme is effective on binary image steganalysis.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. U1736118), the Natural Science Foundation of Guangdong (No. 2016A030313350), the Special Funds for Science and Technology Development of Guangdong (No. 2016KZ010103), the Key Project of Scientific Research Plan of Guangzhou (No. 201804020068), Shanghai Minsheng Science and Technology Support Program (17DZ1205500), Shanghai Sailing Program (17YF1420000), the Fundamental Research Funds for the Central Universities (No. 16lgjc83 and No. 17lgjc45).

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Authors and Affiliations

  1. School of Data and Computer Science, Guangdong Key Laboratory of Information Security Technology, Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing, Sun Yat-sen University, Guangzhou, 510006, China

    Ruipeng Li, Lingwen Zeng, Wei Lu & Junjia Chen

Authors
  1. Ruipeng Li
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  2. Lingwen Zeng
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  3. Wei Lu
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  4. Junjia Chen
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Corresponding author

Correspondence to Wei Lu .

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Editors and Affiliations

  1. Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien, Taiwan

    Ching-Nung Yang

  2. Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien, Taiwan

    Sheng-Lung Peng

  3. University of Canberra, Canberra, ACT, Australia

    Lakhmi C. Jain

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Li, R., Zeng, L., Lu, W., Chen, J. (2020). Binary Image Steganalysis Based on Local Residual Patterns. In: Yang, CN., Peng, SL., Jain, L. (eds) Security with Intelligent Computing and Big-data Services. SICBS 2018. Advances in Intelligent Systems and Computing, vol 895. Springer, Cham. https://doi.org/10.1007/978-3-030-16946-6_74

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