Audio Steganalysis with Improved Convolutional Neural Network.

Deep learning, especially the convolutional neural network (CNN), has enjoyed significant success in many fields, e.g., image recognition. Recently, CNN has successfully applied to multimedia steganalysis. However, the detection performance is still unsatisfactory. In this work, we propose an improved CNN-based method for audio steganalysis. Specifically, a special convolutional layer is first carefully designed, which could capture the minor steganographic noise. Then, a truncated linear unit is adapted to activate the output of shallow convolutional layer. In addition, we employ the average pooling to minimize the over-fitting risk. Finally, a parameter transfer strategy is adopted, aiming to boost the detection performance for the low embedding-rate cases. The experimental results evaluated on 30,000 audio clips verify the effectiveness of our method for a variety of embedding rates. Compared with the existing CNN-based steganalysis methods, our proposed method could achieve superior performance. To facilitate the reproducible research, the source code will be released at GitHub.