Recurrent Convolutional Neural Networks for AMR Steganalysis Based on Pulse Position

With the rapid development of stream multimedia, the adaptive multi-rate (AMR) audio steganography are emerging recently. However, the traditional steganalysis methods face great challenges in detecting short time speech at low embedding rates. To address this problem, we propose a steganalytic scheme by combining Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN), SRCNet. AMR fixed codebook (FCB) steganography embed messages by modifying the pulse positions, which would destroy the FCB correlation. Firstly we analyzed the FCB correlations at different distances, and summarized these correlations into four categories. Furthermore, we utilizes RNN to extract higher level contextual representations of FCBs and CNN to fuse spatial-temporal features for the steganalysis. The proposed approach was evaluated on a public data-set. The experiment results validate that the proposed framework greatly outperforms the existing state-of-the-art methods. The correct detection rate of SRCNet has been improved above at least 10% when the sample is as short as 100ms at the 20% embedding rate. In particular, the network achieves the significant improvements for detecting the STCs based adaptive AMR steganography.