Steganographer detection via deep residual network
Mingjie Zheng, Sheng-hua Zhong, Songtao Wu, Jianmin Jiang · 2017
Steganographer detection problem is to identify culprit actors, who try to hide confidential information with steganography, among many innocent actors. This task has significant challenges, including various embedding steganographic algorithms and payloads, which are usually avoided in steganalysis under laboratory conditions. In this paper, we propose a novel steganographer detection model based on deep residual network. The proposed method strengthens the signal coming from secret messages, which is beneficial for the discrimination between guilty actors and innocent actors. Comprehensive experiments demonstrate that the proposed model achieves very low detection error rates in steganographer detection task. It also outperforms the classical rich model method and other CNN based method. Moreover, the model shows the robustness of inter-steganographic algorithms and inter-payloads.