Revealing Real Face for Generalized Anti-Spoofing

Current researches on Face Anti-Spoofing (FAS) predominantly focus on the detection of spoofing traces in face images. However, these methods may face limitations in generalization since spoofing traces can vary significantly across different domains. Due to poor generalization, which results in overfitting on the training data, cross-domain FAS methods always suffer dramatic degradation. In this paper, our focus is on predicting real faces rather than emphasizing spoofing traces as the primary approach to address the issue. We aim to address the issue of poor generalization by avoiding extracting spoofing traces. Based on this, a generative model named Real Face Generation Net (RFGN) is proposed to predicting real faces according to input face images, so that the final decision can be made by simply calculating the distances between original inputs and outputs. The experimental results show that our proposed method can achieve the state-of-the-art (SOTA) performance with a simple structure.