Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Image Manipulation Detection by Multi-View Multi-Scale Supervision   The key challenge of image manipulation detection is how to learn
generalizable features that are sensitive to manipulations in novel data,
whilst specific to prevent false alarms on authentic images. Current research
emphasizes the sensitivity, with the specificity overlooked. In this paper we
address both aspects by multi-view feature learning and multi-scale
supervision. By exploiting noise distribution and boundary artifact surrounding
tampered regions, the former aims to learn semantic-agnostic and thus more
generalizable features. The latter allows us to learn from authentic images
which are nontrivial to be taken into account by current semantic segmentation
network based methods. Our thoughts are realized by a new network which we term
MVSS-Net. Extensive experiments on five benchmark sets justify the viability of
MVSS-Net for both pixel-level and image-level manipulation detection.

33
The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg