One-Class Face Anti-Spoofing via Spoof Cue Map-Guided Feature Learning

Pei-Kai Huang, Cheng-Hsuan Chiang, Tzu-Hsien Chen, Jun-Xiong Chong, Tyng-Luh Liu, Chiou-Ting Hsu

Abstract: Many face anti-spoofing (FAS) methods have focused on learning discriminative features from both live and spoof training data to strengthen the security of face recognition systems. However, since not every possible attack type is available in the training stage, these FAS methods usually fail to detect unseen attacks in the inference stage. In comparison, one-class FAS, where training data comprise only live faces, aims to detect whether a test face image belongs to the live class or not. In this paper, we propose a novel One-Class Spoof Cue Map estimation Network (OC-SCMNet) to address the one-class FAS detection problem. Our first goal is to learn to extract latent spoof features from live images so that their estimated Spoof Cue Maps (SCMs) should have zero responses. To avoid trapping to a trivial solution, we devise a novel SCM-guided feature learning by combining many SCMs as pseudo ground-truths to guide a conditional generator to create latent spoof features for spoof data. Our second goal is to simulate the potential out-of-distribution spoof attacks approximately. To this end, we propose using a memory bank to dynamically preserve a set of sufficiently “independent” latent spoof features to encourage the generator to probe the latent spoof feature space. Extensive experiments conducted on eight FAS benchmark datasets demonstrate that the proposed OC-SCMNet not only outperforms previous one-class FAS approaches but also achieves performance comparable to the state-of-the-art two-class FAS methods. The code is available at https://github.com/Pei-KaiHuang/CVPR24_OC_SCMNet.