Traditional machine learning-based steganalysis methods on compressed speech
have achieved great success in the field of communication security. However,
previous studies lacked mathematical description and modeling of the
correlation between codewords, and there is still room for improvement in
steganalysis for small-sized and low embedding rates sample. To deal with the
challenge, We use Bayesian networks to measure different types of correlations
between codewords in linear prediction code and present F3SNet — a four-step
strategy: Embedding, Encoding, Attention and Classification for quantizaition
index modulation steganalysis of compressed speech based on Hierarchical
Attention Network. Among them, Embedding converts codewords into high-density
numerical vectors, Encoding uses the memory characteristics of LSTM to retain
more information by distributing it among all its vectors and Attention further
determines which vectors have a greater impact on the final classification
result. To evaluate the performance of F3SNet, we make comprehensive comparison
of F3SNet with existing steganography methods. Experimental results show that
F3SNet surpasses the state-of-the-art methods, particularly for small-sized and
low embedding rate samples.

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Author Of this post: <a href="http://arxiv.org/find/cs/1/au:+Guo_C/0/1/0/all/0/1">Chuanpeng Guo</a>, <a href="http://arxiv.org/find/cs/1/au:+Yang_W/0/1/0/all/0/1">Wei Yang</a>, <a href="http://arxiv.org/find/cs/1/au:+Huang_L/0/1/0/all/0/1">Liusheng Huang</a>

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