Collaborative Feature Learning for Fine-grained Facial Forgery Detection and Segmentation
This addresses the need for more precise localization of manipulated facial components in digital forensics, representing an incremental improvement over previous work focused on entire faces.
The paper tackles the problem of detecting and segmenting fine-grained facial forgeries by proposing a collaborative feature learning approach that simultaneously performs detection and segmentation, achieving better overall performance than state-of-the-art methods.
Detecting maliciously falsified facial images and videos has attracted extensive attention from digital-forensics and computer-vision communities. An important topic in manipulation detection is the localization of the fake regions. Previous work related to forgery detection mostly focuses on the entire faces. However, recent forgery methods have developed to edit important facial components while maintaining others unchanged. This drives us to not only focus on the forgery detection but also fine-grained falsified region segmentation. In this paper, we propose a collaborative feature learning approach to simultaneously detect manipulation and segment the falsified components. With the collaborative manner, detection and segmentation can boost each other efficiently. To enable our study of forgery detection and segmentation, we build a facial forgery dataset consisting of both entire and partial face forgeries with their pixel-level manipulation ground-truth. Experiment results have justified the mutual promotion between forgery detection and manipulated region segmentation. The overall performance of the proposed approach is better than the state-of-the-art detection or segmentation approaches. The visualization results have shown that our proposed model always captures the artifacts on facial regions, which is more reasonable.