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.

翻译：人脸识别系统频繁遭受多种不同类型的物理攻击与数字攻击。现有方法在分别应对物理攻击和数字攻击的场景中已取得满意性能，但鲜有研究考虑构建可同时处理两类攻击的统一模型，这导致需要开发并维护多个模型。为实现单一模型对物理攻击和数字攻击的联合检测，我们提出了一种创新方法，该方法可适配任意网络架构。本方法主要包含两种数据增强策略：模拟物理伪造线索增强（SPSC）与模拟数字伪造线索增强（SDSC）。SPSC和SDSC通过分别模拟物理攻击和数字攻击的伪造线索，将活体样本增强为模拟攻击样本，显著提升了模型对"未见"攻击类型的检测能力。大量实验表明，SPSC与SDSC可分别在UniAttackData数据集的协议2.1和2.2中实现最优泛化性能。我们的方法在第五届人脸反欺骗挑战赛@CVPR2024的"统一物理-数字人脸攻击检测"赛道中荣获第一名。最终提交结果分别获得3.75%的APCER、0.93%的BPCER和2.34%的ACER。相关代码已开源：https://github.com/Xianhua-He/cvpr2024-face-anti-spoofing-challenge。