Generalizing Face Forgery Detection via Uncertainty Learning

Current face forgery detection methods have made significant progress in achieving high intra-dataset accuracy by building a deterministic binary detector. However, deterministic networks cannot effectively capture noise and distribution shifts in the input, which makes them less robust and prone to poor generalization in real-world scenarios. To address this problem, in this paper, we propose an Uncertainty-Aware Learning (UAL) method for face forgery detection. Specifically, we extend the Transformer model in a probabilistic manner by modeling dependencies between patches as Gaussian random variables. Additionally, we introduce a Patch Selection Module that can efficiently and accurately identify discriminative regions with high-uncertainty information, which are further utilized for final classification. Furthermore, with the quantified uncertainty of the entire image, we design a novel Uncertainty-Aware One-Center Loss that enhances intra-class compactness for genuine faces only, thereby improving the inter-class separability in the embedding space. We conduct extensive experiments to demonstrate the effectiveness of the proposed method, and the results verify that, our Uncertainty-Aware Learning method enjoys better robustness and generalization ability comparing against other state-of-the-art methods.