  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 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.
