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High-Performance Linguistic Steganalysis, Capacity Estimation and Steganographic Positioning

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Digital Forensics and Watermarking (IWDW 2020)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12617))

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Abstract

With the rapid development of natural language processing technology, various linguistic steganographic methods have been proposed increasingly, which may bring great challenges in the governance of cyberspace security. The previous linguistic steganalysis methods based on neural networks with word embedding layer could only extract the context-independent word-level features, which are insufficient for capturing the complex semantic dependencies in sentences, thus may limit the performance of text steganalysis. In this paper, we propose a novel linguistic steganalysis model. We first employ the BERT or Glove component to extract the contextualized association relationships of words in the sentences. Then we put these extracted features into BiLSTM to further get context information. We use the attention mechanism to find out local parts that may be discordant in text. Finally, based on these extracted features, we use the softmax classifier to decide if the input sentence is cover or stego. Experimental results show that the proposed model can achieve currently the best performance of text steganalysis and hidden capacity estimation. Further experiments found that proposed model can even locate where the secret information may be embedded in the text to a certain extent. To the best of our knowledge, we made the first attempt to achieve text steganography positioning in the field of text steganalysis (Code and datasets are available at https://github.com/YangzlTHU/Linguistic-Steganography-and-Steganalysis).

Supported by the National Key Research and Development Program of China under Grant No. 2018YFB0804103 and the National Natural Science Foundation of China (No. U1936208, No. 61862002 and No. U1936216).

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Notes

  1. 1.

    Pre-trained word embedding of GloVe can be downloaded from http://nlp.stanford.edu/projects/glove/.

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Author information

Authors and Affiliations

  1. Beijing National Research Center for Information Science and Technology, Beijing, China

    Jiajun Zou, Zhongliang Yang, Siyu Zhang & Yongfeng Huang

  2. Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China

    Jiajun Zou, Zhongliang Yang, Siyu Zhang & Yongfeng Huang

  3. Department of Computer Science, Namal Institute, Mianwali, 42250, Pakistan

    Sadaqat ur Rehman

Authors
  1. Jiajun Zou
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  2. Zhongliang Yang
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  3. Siyu Zhang
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  4. Sadaqat ur Rehman
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  5. Yongfeng Huang
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Corresponding author

Correspondence to Zhongliang Yang .

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Editors and Affiliations

  1. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Xianfeng Zhao

  2. Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, USA

    Yun-Qing Shi

  3. Department of Information Engineering, University of Florence, Florence, Italy

    Alessandro Piva

  4. School of Cybersecurity, Korea University, Seoul, Korea (Republic of)

    Hyoung Joong Kim

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Zou, J., Yang, Z., Zhang, S., Rehman, S.u., Huang, Y. (2021). High-Performance Linguistic Steganalysis, Capacity Estimation and Steganographic Positioning. In: Zhao, X., Shi, YQ., Piva, A., Kim, H.J. (eds) Digital Forensics and Watermarking. IWDW 2020. Lecture Notes in Computer Science(), vol 12617. Springer, Cham. https://doi.org/10.1007/978-3-030-69449-4_7

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  • DOI: https://doi.org/10.1007/978-3-030-69449-4_7

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