Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Steganalysis: End-to-End Learning with Supervisory Information
  beyond Class Labels   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.

35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
24
The categories of this paper are: dg-ga
20
The categories of this paper are: acc-phys