  With the Volume of Voice over IP (VoIP) traffic rises shapely, more and more
VoIP-based steganography methods have emerged in recent years, which poses a
great threat to the security of cyberspace. Low bit-rate speech codecs are
widely 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 or
inner-frame correlation features in code-words but ignore the hierarchical
structure which exists in speech frame. In this paper, motivated by the complex
multi-scale structure, we design a Hierarchical Representation Network to
tackle the steganalysis of QIM steganography in low-bit-rate speech signal. In
the proposed model, Convolution Neural Network (CNN) is used to model the
hierarchical structure in the speech frame, and three level of attention
mechanisms are applied at different convolution block, enabling it to attend
differentially to more and less important content in speech frame. Experiments
demonstrated that the steganalysis performance of the proposed method can
outperforms the state-of-the-art methods especially in detecting both short and
low embeded speech samples. Moreover, our model needs less computation and has
higher time efficiency to be applied to real online services.
