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Segmentation Based Steganalysis of Spatial Images Using Local Linear Transform

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Part of the book series:Lecture Notes in Computer Science ((LNSC,volume 10082))

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Abstract

Most of the traditional image steganalysis techniques are conducted on the entire image and do not take advantage of the content diversity. However, the steganalysis features are affected by image content, and the impact is more serious than embedding. This makes steganalysis to be a classification problem with bigger within-class scatter distances and smaller between-class scatter distances. In this paper, a steganalysis algorithm aimed at spatial steganographic methods which can reduce the differences of image statistical characteristics caused by image content is proposed. The given images are segmented to sub-images according to the texture complexity. Steganalysis features based on local linear transform are separately extracted from each sort of sub-images with the same or close texture complexity to build a classifier. The final steganalysis result is figured out through a weighted fusing process. Experimental results performed on several diverse image databases and circumstances demonstrate that the proposed method exhibits excellent performances.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China under grant No. 61272490 and No. 61602511. The authors would like to thank the reviewers for their insightful comments and helpful suggestions.

Author information

Authors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Ran Wang & Shaozhang Niu

  2. Zhengzhou Information Science and Technology Institute, Zhengzhou, China

    Ran Wang, Xijian Ping & Tao Zhang

  3. Zhengzhou Shengda University of Economics, Business and Management, Xinzheng, China

    Xijian Ping

Authors
  1. Ran Wang

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  2. Xijian Ping

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  3. Shaozhang Niu

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  4. Tao Zhang

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Corresponding author

Correspondence toRan Wang.

Editor information

Editors and Affiliations

  1. New Jersey Institute of Technology, Newark, New Jersey, USA

    Yun Qing Shi

  2. Korea University, Seoul, Korea (Republic of)

    Hyoung Joong Kim

  3. University of Vigo, Vigo, Spain

    Fernando Perez-Gonzalez

  4. Chinese Academy of Sciences, Beijing, China

    Feng Liu

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Wang, R., Ping, X., Niu, S., Zhang, T. (2017). Segmentation Based Steganalysis of Spatial Images Using Local Linear Transform. In: Shi, Y., Kim, H., Perez-Gonzalez, F., Liu, F. (eds) Digital Forensics and Watermarking. IWDW 2016. Lecture Notes in Computer Science(), vol 10082. Springer, Cham. https://doi.org/10.1007/978-3-319-53465-7_40

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