Practical Face Swapping Detection Based on Identity Spatial Constraints

The generalization of face swapping detectors against unseen face manipulation methods is important to practical applications. Most existing methods based on convolutional neural networks (CNN) simply map the facial images to real/fake binary labels and achieve high performance on the known forgeries, but they almost fail to detect new manipulation methods. In order to improve the generalization of face swapping detection, this work concentrates on a practical scenario to protect specific persons by proposing a novel face swapping detector requiring a reference image. To this end, we design a new detection framework based on identity spatial constraints (DISC), which consists of a backbone network and an identity semantic encoder (ISE). When inspecting an image of a particular person, the ISE utilizes a real facial image of that person as the reference to constrain the backbone to focus on the identity-related facial areas, so as to exploit the intrinsic discriminative clues to the forgery in the query image. Cross-dataset evaluations on five large-scale face forgery datasets show that DISC significantly improves the performance against unseen manipulation methods and is robust against the distortions. Compared to the existing detection methods, the AUC scores achieve 10%~40% performance improvements.