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With the Volume of Voice over IP (VoIP) traffic rises shapely, more and moreVoIP-based steganography methods have emerged in recent years, which poses agreat threat to the security of cyberspace. Low bit-rate speech codecs arewidely used in the VoIP application due to its powerful compression capability.QIM steganography makes it possible to hide secret information in VoIP streams.Previous research mostly focus on capturing the inter-frame correlation orinner-frame correlation features in code-words but ignore the hierarchicalstructure which exists in speech frame. In this paper, motivated by the complexmulti-scale structure, we design a Hierarchical Representation Network totackle the steganalysis of QIM steganography in low-bit-rate speech signal. Inthe proposed model, Convolution Neural Network (CNN) is used to model thehierarchical structure in the speech frame, and three level of attentionmechanisms are applied at different convolution block, enabling it to attenddifferentially to more and less important content in speech frame. Experimentsdemonstrated that the steganalysis performance of the proposed method canoutperforms the state-of-the-art methods especially in detecting both short andlow embeded speech samples. Moreover, our model needs less computation and hashigher time efficiency to be applied to real online services.

Hierarchical Representation Network for Steganalysis of QIM Steganography in Low-Bit-Rate Speech Signals