VIPL's paper on cross-domain face anti-spoofing is accepted by TPAMI

Congratulations! VIPL's paper on cross-domain face anti-spoofing “Generalized Face Liveness Detection via De-fake Face Generator” (Authors: Xingming Long, Jie Zhang*, Shiguang Shan) was accepted by T-PAMI. IEEE T-PAMI, i.e., IEEE Transactions on Pattern Analysis and Machine Intelligence is a CCF-ranked-A top-tier Artificial Intelligence journal with a high IF score of 20.8, announced in 2024.

Previous Face Anti-spoofing (FAS) methods face the challenge of generalizing to unseen domains, mainly because most existing FAS datasets are relatively small and lack data diversity. Thanks to the development of face recognition in the past decade, numerous real face images are available publicly, which are however neglected previously by the existing literature. In this paper, we propose an Anomalous cue Guided FAS (AG-FAS) method, which can effectively leverage large-scale additional real faces for improving model generalization via a De-fake Face Generator (DFG). Specifically, by training on a large-scale real face only dataset, the generator obtains the knowledge of what a real face should be like, and thus has the capability of generating a “real” version of any input face image. Consequently, the difference between the input face and the generated “real” face can be treated as cues of attention for the fake feature learning. With the above ideas, an Off-real Attention Network (OA-Net) is proposed which allocates its attention to the spoof region of the input according to the anomalous cue. Extensive experiments on a total of nine public datasets show our method achieves state-of-the-art results under cross-domain evaluations with unseen scenarios and unknown presentation attacks. Besides, we provide theoretical analysis demonstrating the effectiveness of the proposed anomalous cues.

Fig. 1: The structure of the proposed De-fake Face Generator (DFG). The DFG trained on a large-scale real face dataset can generate a corresponding “real” face of any given input face when taking the input's identity feature as guidance.