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
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.