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A Common Steganalysis Method of Low Embedding Rate Steganography in Compressed Speech Based on Hierarchy Feature Extraction and Fusion

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Advances in Artificial Intelligence and Security (ICAIS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1424))

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Abstract

The current steganalysis researches have not specifically explored the compressed speech stream with 1%–9% embedding rates. In this paper, we propose a common steganalysis method of low embedding rate steganography based on hierarchy feature extraction and fusion. Firstly, codewords in each frame are converted to a multi-hot vector. And each multi-hot vector will be mapped into a fixed length embedding vector to get a more compact representation by utilizing the pre-trained dictionaries. Then, a hierarchy feature extraction and fusion framework is employed to perform the extraction and fusion of different levels of correlation features. Specifically, a 5-layer convolutional neural network is used to extract correlation feature information from local to global. The features of different local scales are restored to the same size by the transposed convolution. In addition, the attention mechanism is introduced in different layers of the network to assign different importance weights to the output feature within each layer. Finally, the prediction results can be generated by the fully connected layer. Experimental results show that our method performs better than the existing steganalysis methods for detecting multiple steganographies in the low bit-rate compressed speech streams. On the mixed dataset of multiple steganography methods, the proposed method can reach 73.56% on the speech stream under 5% embedding rate. And the accuracy can exceed 83% on the dataset under 9% embedding rate.

This work was supported in part by the Important Science and Technology Project of Hainan Province under Grant ZDKJ201807, in part by the Hainan Provincial Natural Science Foundation of China under Grant 618QN309, in part by the Scientific Research Foundation Project of Haikou Laboratory, Institute of Acoustics, Chinese Academy of Sciences, and in part by the IACAS Young Elite Researcher Project under GrantQNYC201829 and Grant QNYC201747.

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Authors and Affiliations

  1. Institute of Acoustics, Chinese Academy of Sciences, Beijing, 100190, China

    Songbin Li, Jingang Wang, Qiandong Yan, Peng Liu & Miao Wei

Authors
  1. Songbin Li
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  2. Jingang Wang
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  3. Qiandong Yan
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  4. Peng Liu
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  5. Miao Wei
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Corresponding author

Correspondence to Songbin Li .

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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

    Xiaorui Zhang

  3. Jinan University, Guangzhou, China

    Zhihua Xia

  4. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Li, S., Wang, J., Yan, Q., Liu, P., Wei, M. (2021). A Common Steganalysis Method of Low Embedding Rate Steganography in Compressed Speech Based on Hierarchy Feature Extraction and Fusion. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Advances in Artificial Intelligence and Security. ICAIS 2021. Communications in Computer and Information Science, vol 1424. Springer, Cham. https://doi.org/10.1007/978-3-030-78621-2_26

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  • DOI: https://doi.org/10.1007/978-3-030-78621-2_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78620-5

  • Online ISBN: 978-3-030-78621-2

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