LAUSR

Steganography has made great progress over the past few years due to the advancement of deep convolutional neural networks (DCNNs), which have been successfully used to multi-domains. Correspondingly the performance of steganalysis models inevitably encounters a bottleneck since the CNN based steganography models perform better. In this paper, we propose an Advanced General convolutional neural Network for steganalysis (AG-Net) to address this problem. We firstly design a confrontation module to extract and compare features of cover and stego images, which are captured from an unknown steganography network. Then, we construct the association between two adjacent confrontation modules according to the feature comparison of the previous module, to accumulate the differences of mid- and high-level features between the cover and stego images. Thirdly, we deliver the loss of the last confrontation module to a softmax layer after batch normalization and scalarization, to classify and detect stego images. Extensive experiments and evaluations demonstrate that the proposed AG-Net can achieve promising performance in response to different challenging steganographic algorithms.