Steganographer detection based on multiclass dilated residual networks

Steganographer detection task is to identify criminal users, who attempt to conceal confidential information by steganography methods, among a large number of innocent users. The significant challenge of the task is how to collect the evidences to identify the guilty user with suspicious images, which are embedded with secret messages generating by unknown steganography and payload. Unfortunately, existing methods for steganalysis were served for the binary classification. It makes them harder to classify the images with different kinds of payloads, especially when the payloads of images in test dataset have not been provided in advance. In this paper, we propose a novel steganographer detection method based on multiclass deep neural networks. In the training stage, the networks are trained to classify the images with six types of payloads. The networks can preserve even strengthen the weak stego signals from secret messages in much larger receptive filed by virtue of residual and dilated residual learning. In the inference stage, the learnt model is used to extract the discriminative features, which can capture the difference between guilty users and innocent users. A series of empirical experimental results demonstrate that the proposed method achieves good performance in spatial and frequency domains even though the embedding payload is low. The proposed method achieves a higher level of robustness of inter-steganographic algorithms and can provide a possible solution to address the payload mismatch problem.