Image Manipulation Detection by Multi-View Multi-Scale Supervision
- Resource Type
- Conference
- Authors
- Chen, Xinru; Dong, Chengbo; Ji, Jiaqi; Cao, Juan; Li, Xirong
- Source
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV) ICCV Computer Vision (ICCV), 2021 IEEE/CVF International Conference on. :14165-14173 Oct, 2021
- Subject
- Computing and Processing
Representation learning
Image segmentation
Computer vision
Sensitivity
Costs
Image edge detection
Semantics
Image and video manipulation detection and integrity methods
- Language
- ISSN
- 2380-7504
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.