Author:
Liu Shice,Lu Shitao,Xu Hongyi,Yang Jing,Ding Shouhong,Ma Lizhuang
Abstract
Although existing face anti-spoofing (FAS) methods achieve high accuracy in intra-domain experiments, their effects drop severely in cross-domain scenarios because of poor generalization. Recently, multifarious techniques have been explored, such as domain generalization and representation disentanglement. However, the improvement is still limited by two issues: 1) It is difficult to perfectly map all faces to a shared feature space. If faces from unknown domains are not mapped to the known region in the shared feature space, accidentally inaccurate predictions will be obtained. 2) It is hard to completely consider various spoof traces for disentanglement. In this paper, we propose a Feature Generation and Hypothesis Verification framework to alleviate the two issues. Above all, feature generation networks which generate hypotheses of real faces and known attacks are introduced for the first time in the FAS task. Subsequently, two hypothesis verification modules are applied to judge whether the input face comes from the real-face space and the real-face distribution respectively. Furthermore, some analyses of the relationship between our framework and Bayesian uncertainty estimation are given, which provides theoretical support for reliable defense in unknown domains. Experimental results show our framework achieves promising results and outperforms the state-of-the-art approaches on extensive public datasets.
Publisher
Association for the Advancement of Artificial Intelligence (AAAI)
Cited by
10 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Dual Sampling Based Causal Intervention for Face Anti-Spoofing With Identity Debiasing;IEEE Transactions on Information Forensics and Security;2024
2. Face Presentation Attack Detection;Handbook of Face Recognition;2023-12-30
3. Multi-domain mixup for scenario-universal face anti-spoofing;Computers & Graphics;2023-11
4. Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofing;2023 Twelfth International Conference on Image Processing Theory, Tools and Applications (IPTA);2023-10-16
5. Generalized Face Anti-Spoofing via Multi-Task Learning and One-Side Meta Triplet Loss;2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG);2023-01-05