Joint Physical-Digital Facial Attack Detection Via Simulating Spoofing Clues

Xianhua He, Dashuang Liang, Song Yang, Zhanlong Hao, Hui Ma, Binjie Mao, Xi Li, Yao Wang, Pengfei Yan, Ajian Liu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 995-1004

Face recognition systems are frequently subjected to a variety of physical and digital attacks of different types. Previous methods have achieved satisfactory performance in scenarios that address physical attacks and digital attacks respectively. However few methods are considered to integrate a model that simultaneously addresses both physical and digital attacks implying the necessity to develop and maintain multiple models. To jointly detect physical and digital attacks within a single model we propose an innovative approach that can adapt to any network architecture. Our approach mainly contains two types of data augmentation which we call Simulated Physical Spoofing Clues augmentation (SPSC) and Simulated Digital Spoofing Clues augmentation (SDSC). SPSC and SDSC augment live samples into simulated attack samples by simulating spoofing clues of physical and digital attacks respectively which significantly improve the capability of the model to detect "unseen" attack types. Extensive experiments show that SPSC and SDSC can achieve state-of-the-art generalization in Protocols 2.1 and 2.2 of the UniAttackData dataset respectively. Our method won first place in "Unified Physical-Digital Face Attack Detection" of the 5th Face Anti-spoofing Challenge@CVPR2024. Our final submission obtains 3.75% APCER 0.93% BPCER and 2.34% ACER respectively. Our code is available at https://github.com/Xianhua-He/cvpr2024-face-anti-spoofing-challenge.