Content-adaptive selective steganographer detection via embedding probability estimation deep networks

Steganographer detection is to detect culprit users, who attempt to hide confidential information with steganography, among many innocent users. By incorporating the knowledge of true embedding probability map that illustrates the probability distribution of embedding messages in the corresponding image, content-adaptive steganography and steganalysis have made great progress. Unfortunately, true embedding probability map is inappropriate for steganographer detection method due to the significant challenges that the steganographic algorithm and the embedding payload are usually unknown in the task of steganographer detection. In this paper, we propose a novel content-adaptive selective steganographer detection method incorporated with learning-based embedding probability estimation. The embedding probability estimation is first formulated as a pixel-wise segmentation and recognition problem and is integrated into multi-class dilated residual learning model to extract the discriminative features. In the end, the steganographer is identified by local factor outlier with the selective strategy. Extensive experiments demonstrate that the estimated embedding probability map shows robustness against different steganographic algorithms and different payloads. From our experiments, we also find that the proposed content-adaptive selective steganographer detection framework integrated by the estimated embedding probability map achieves low detection error rates in both spatial and frequency domains.