Deep Steganalysis: End-to-End Learning with Supervisory Information beyond Class Labels
This work addresses steganalysis for security applications, but it is incremental as it builds on existing deep learning approaches by adding pixel-level supervision.
The authors tackled the problem of deep learning for steganalysis by proposing an end-to-end framework that learns features directly from raw images and automatically learns high-pass filters, using additional pixel-level supervision from cover-stego image pairs. The experimental results demonstrated the effectiveness of this architecture.
Recently, deep learning has shown its power in steganalysis. However, the proposed deep models have been often learned from pre-calculated noise residuals with fixed high-pass filters rather than from raw images. In this paper, we propose a new end-to-end learning framework that can learn steganalytic features directly from pixels. In the meantime, the high-pass filters are also automatically learned. Besides class labels, we make use of additional pixel level supervision of cover-stego image pair to jointly and iteratively train the proposed network which consists of a residual calculation network and a steganalysis network. The experimental results prove the effectiveness of the proposed architecture.