  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.
