Protecting digital identities of human face from various attack vectors is
paramount, and face anti-spoofing plays a crucial role in this endeavor.
Current approaches primarily focus on detecting spoofing attempts within
individual frames to detect presentation attacks. However, the emergence of
hyper-realistic generative models capable of real-time operation has heightened
the risk of digitally generated attacks. In light of these evolving threats,
this paper aims to address two key aspects. First, it sheds light on the
vulnerabilities of state-of-the-art face anti-spoofing methods against digital
attacks. Second, it presents a comprehensive taxonomy of common threats
encountered in face anti-spoofing systems. Through a series of experiments, we
demonstrate the limitations of current face anti-spoofing detection techniques
and their failure to generalize to novel digital attack scenarios. Notably, the
existing models struggle with digital injection attacks including adversarial
noise, realistic deepfake attacks, and digital replay attacks. To aid in the
design and implementation of robust face anti-spoofing systems resilient to
these emerging vulnerabilities, the paper proposes key design principles from
model accuracy and robustness to pipeline robustness and even platform
robustness. Especially, we suggest to implement the proactive face
anti-spoofing system using active sensors to significant reduce the risks for
unseen attack vectors and improve the user experience.

面部反欺骗、数字攻击、漏洞、威胁与鲁棒性是本研究的关键词。通过一系列实验，本文揭示了当前面部反欺骗检测技术的限制和其对新型数字攻击场景的失败。为了抵御这些新型攻击的漏洞，本文提出从模型准确性、鲁棒性到流程鲁棒性和平台鲁棒性的关键设计原则。尤其是，我们建议使用主动传感器来实现预防性面部反欺骗系统，以显著降低未知攻击向量的风险并提高用户体验。

设计鲁棒远程人脸反欺诈系统原则

Principles of Designing Robust Remote Face Anti-Spoofing Systems

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

为了同时检测物理攻击和数字攻击，我们提出了一种创新的方法，该方法采用两种类型的数据增强：模拟物理欺骗线索增强和模拟数字欺骗线索增强，显著提高了模型对 "未知" 攻击类型的检测能力。

通过模拟欺骗线索的手段进行联合物理数字面部攻击检测

Joint Physical-Digital Facial Attack Detection Via Simulating Spoofing  Clues

Face Recognition (FR) systems can suffer from physical (i.e., print photo)
and digital (i.e., DeepFake) attacks. However, previous related work rarely
considers both situations at the same time. This implies the deployment of
multiple models and thus more computational burden. The main reasons for this
lack of an integrated model are caused by two factors: (1) The lack of a
dataset including both physical and digital attacks with ID consistency which
means the same ID covers the real face and all attack types; (2) Given the
large intra-class variance between these two attacks, it is difficult to learn
a compact feature space to detect both attacks simultaneously. To address these
issues, we collect a Unified physical-digital Attack dataset, called
UniAttackData. The dataset consists of $1,800$ participations of 2 and 12
physical and digital attacks, respectively, resulting in a total of 29,706
videos. Then, we propose a Unified Attack Detection framework based on
Vision-Language Models (VLMs), namely UniAttackDetection, which includes three
main modules: the Teacher-Student Prompts (TSP) module, focused on acquiring
unified and specific knowledge respectively; the Unified Knowledge Mining (UKM)
module, designed to capture a comprehensive feature space; and the Sample-Level
Prompt Interaction (SLPI) module, aimed at grasping sample-level semantics.
These three modules seamlessly form a robust unified attack detection
framework. Extensive experiments on UniAttackData and three other datasets
demonstrate the superiority of our approach for unified face attack detection.

该研究论文提出了一个基于视觉语言模型的统一攻击检测框架 (UniAttackDetection)，包括三个主要模块：教师 - 学生提示 (TSP) 模块、统一知识挖掘 (UKM) 模块和样本级提示交互 (SLPI) 模块，这些模块共同构成了一个强大的统一人脸攻击检测框架。通过对 UniAttackData 和其他三个数据集的广泛实验，证明了该方法在统一人脸攻击检测方面的优越性。