ConfR: Conflict Resolving for Generalizable Deepfake Detection
Jin Chen, Jiahe Tian, Yu Cai, Xi Wang, Zhaoxing Li, Yesheng Chai, Jiao Dai, Jizhong Han · 2024
Deepfake detectors often encounter performance degradation when tested on unseen forgery methods. Existing literature tries to capture common features among multiple source forgery domains. However, we show that conflict arises in the shared feature space when each domain expresses domain-specific bias. If left unresolved, this conflict might mislead the model to learn domain-specific features and lead to inferior generalization. In this paper, we propose a new learning approach, Conflict Resolving (ConfR), designed to minimize conflict and learn features that generalize across forgeries. ConfR incorporates two key elements: the Intra-Domain Consistency Preserving (ICP) loss ensures updating consistency within forgery types, and the Inter-Domain Conflict Resolving (ICR) Module resolves updating conflicts between different forgery types. Extensive experiments demonstrate that ConfR significantly improves upon the state-of-the-art method, highlighting its potential for more generalizable deepfake detection.