Abstract
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 this https URL.
Abstract (translated)
面部识别系统常常会受到各种类型的物理和数字攻击。之前的方法在处理物理攻击和数字攻击的场景方面都取得了相当不错的性能。然而，很少有方法被认为整合了一个同时处理物理和数字攻击的模型，这表明需要开发和维护多个模型。为了在单个模型中共同检测物理和数字攻击，我们提出了一个创新的方法，可以适应任何网络架构。我们主要包含两种数据增强类型，我们称之为模拟物理 spoofing 线索增强（SPSC）和模拟数字 spoofing 线索增强（SDSC）。SPSC和SDSC通过模拟物理和数字攻击的 spoofing 线索将实时样本转换为模拟攻击样本，显著提高了模型的检测“未见”攻击类型的能力。广泛的实验证明，SPSC和SDSC可以在UniAttackData数据集中的Protocol 2.1和2.2上实现最先进的泛化。我们的方法在2024年CVPR的“统一物理-数字面部攻击检测”挑战中获得第一。我们的最终提交获得了3.75%的APCER，0.93%的BPCER和2.34%的ACER。我们的代码可在此处下载：https://www.thunlock.org/thunlock/。
URL
https://arxiv.org/abs/2404.08450
