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Text Semantic Steganalysis Based on Word Embedding

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Cloud Computing and Security (ICCCS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11066))

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Abstract

Most state-of-the-art detection methods against synonym substitution based steganography extract features based on statistical distortion. However, synonym substitution will cause not only statistical distortion but also semantic distortion. In this paper, we propose word embedding feature (WEF) to detect the semantic distortion. Furthermore, a fused feature called word embedding and statistical feature set (WESF) which consists of WEF and statistical feature based on word frequency is designed to improve detection performance. Experiments show that WESF can achieve lower detection error rates compared with prmethods.

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Acknowledgements

This work was supported in part by the Natural Science Foundation of China under Grant U1636201, 61572452.

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Authors and Affiliations

  1. CAS Key Laboratory of Electromagnetic Space Information, University of Science and Technology of China, Hefei, China

    Xin Zuo, Huanhuan Hu, Weiming Zhang & Nenghai Yu

Authors
  1. Xin Zuo
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  2. Huanhuan Hu
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  3. Weiming Zhang
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  4. Nenghai Yu
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Corresponding author

Correspondence to Weiming Zhang .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Zhaoqing Pan

  3. Department of Computer Science, Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Zuo, X., Hu, H., Zhang, W., Yu, N. (2018). Text Semantic Steganalysis Based on Word Embedding. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11066. Springer, Cham. https://doi.org/10.1007/978-3-030-00015-8_42

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  • DOI: https://doi.org/10.1007/978-3-030-00015-8_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00014-1

  • Online ISBN: 978-3-030-00015-8

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