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International Conference on Machine Learning and Intelligent Communications

MLICOM 2017: Machine Learning and Intelligent Communications pp 145–157Cite as

A New Universal Steganalyzer for JPEG Images

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Abstract

The JPEG (Joint Photographic Experts Group) file format is currently one of the most widely used image formats. The study on JPEG steganography and steganalysis is a hotspot in the field of information hiding. With the matrix coding and some new adaptive embedding strategies having been put forward, the detection of stego images is becoming more and more difficult. In recent years, a series of new feature extraction methods have been proposed in the field of steganalysis. However, the detection accuracy rate can only be increased by 1–2% points or even less. Based on those existing steganalytic algorithms, a new feature merging method is proposed in this paper. Via merging features extracted from different domains, the detection accuracy rate of those existing JPEG steganalytic algorithms can be improved by 3% points or even higher. Considering about that the feature dimension is so high after feature merging and thus it may bring difficulties in the feature extraction, training and classification process, a new feature selection method is also proposed in this paper. Experimental results demonstrate that it can not only achieve reduction of the dimensionality, but also maintain a high detection accuracy rate.

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Acknowledgements

This work is partially supported by National Natural Science Foundation of China (61772572), Natural Science Foundation of Guangdong Province of China (2017A030313366), and the Fundamental Research Funds for the Central Universities (17lgjc45).

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Authors and Affiliations

  1. School of Electronics and Information Technology, Sun Yat-Sen University, Guangzhou, 510006, China

    Ge Liu & Zhonghua Li

  2. School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, 510006, China

    Fangjun Huang & Qi Chen

  3. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, China

    Fangjun Huang

Authors
  1. Ge Liu
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  2. Fangjun Huang
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  3. Qi Chen
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  4. Zhonghua Li
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Corresponding author

Correspondence to Fangjun Huang .

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Editors and Affiliations

  1. Harbin Institute of Technology, Harbin, Heilongjiang, China

    Xuemai Gu

  2. Harbin Institute of Technology, Weihai, Heilongjiang, China

    Gongliang Liu

  3. Shandong University, Weihai, Heilongjiang, China

    Bo Li

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Liu, G., Huang, F., Chen, Q., Li, Z. (2018). A New Universal Steganalyzer for JPEG Images. In: Gu, X., Liu, G., Li, B. (eds) Machine Learning and Intelligent Communications. MLICOM 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 226. Springer, Cham. https://doi.org/10.1007/978-3-319-73564-1_15

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  • DOI: https://doi.org/10.1007/978-3-319-73564-1_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73563-4

  • Online ISBN: 978-3-319-73564-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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