Volume 32 Issue 1
Jan.  2023
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WEN Juan, DENG Yaqian, PENG Wanli, et al., “Linguistic Steganalysis via Fusing Multi-Granularity Attentional Text Features,” Chinese Journal of Electronics, vol. 32, no. 1, pp. 76-84, 2023, doi: 10.23919/cje.2022.00.009
Citation: WEN Juan, DENG Yaqian, PENG Wanli, et al., “Linguistic Steganalysis via Fusing Multi-Granularity Attentional Text Features,” Chinese Journal of Electronics, vol. 32, no. 1, pp. 76-84, 2023, doi: 10.23919/cje.2022.00.009

Linguistic Steganalysis via Fusing Multi-Granularity Attentional Text Features

doi: 10.23919/cje.2022.00.009
Funds:  This work was supported by the National Natural Science Foundation of China (61872368, 61802410)
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  • Author Bio:

    Juan WEN received the B.E. degree in information engineering and Ph.D. degree in signal and information processing from Beijing University of Posts and Telecommunications. She is now an Associate Professor at China Agricultural University. Her research interests include artificial intelligence, information hiding, and natural language processing. (Email: wenjuan@cau.edu.cn)

    Yaqian DENG is currently pursuing the M.E. degree in computer technology with the College of Information and Electrical Engineering, China Agricultural University. Her research interest includes information hiding. (Email: dengyaqian@cau.edu.cn)

    Wanli PENG received the B.E. degree from College of Physics and Electronic Engineering, Harbin Normal University, China, in 2018. He is currently pursuing the Ph.D. degree with the College of Information and Electrical Engineering, China Agricultural University, Beijing, China. His research interests include information hiding and natural language processing. (Email: hunanpwl@cau.edu.cn)

    Yiming XUE (corresponding author) is currently a Professor in the College of Information and Electrical Engineering, China Agricultural University. His research interests include multimedia processing, multimedia security, and VLSI design. (Email: xueym@cau.edu.cn)

  • Received Date: 2022-01-17
  • Accepted Date: 2022-05-13
  • Available Online: 2022-07-04
  • Publish Date: 2023-01-05
  • Deep learning based language models have improved generation-based linguistic steganography, posing a huge challenge for linguistic steganalysis. The existing neural-network-based linguistic steganalysis methods are incompetent to deal with complicated text because they only extract single-granularity features such as global or local text features. To fuse multi-granularity text features, we present a novel linguistic steganalysis method based on attentional bidirectional long-short-term-memory (BiLSTM) and short-cut dense convolutional neural network (CNN). The BiLSTM equipped with the scaled dot-product attention mechanism is used to capture the long dependency representations of the input sentence. The CNN with the short-cut and dense connection is exploited to extract sufficient local semantic features from the word embedding matrix. We connect two structures in parallel, concatenate the long dependency representations and the local semantic features, and classify the stego and cover texts. The results of comparative experiments demonstrate that the proposed method is superior to the state-of-the-art linguistic steganalysis.
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