Face Anti-Spoofing (FAS) is crucial to safeguard Face Recognition (FR)
Systems. In real-world scenarios, FRs are confronted with both physical and
digital attacks. However, existing algorithms often address only one type of
attack at a time, which poses significant limitations in real-world scenarios
where FR systems face hybrid physical-digital threats. To facilitate the
research of Unified Attack Detection (UAD) algorithms, a large-scale
UniAttackData dataset has been collected. UniAttackData is the largest public
dataset for Unified Attack Detection, with a total of 28,706 videos, where each
unique identity encompasses all advanced attack types. Based on this dataset,
we organized a Unified Physical-Digital Face Attack Detection Challenge to
boost the research in Unified Attack Detections. It attracted 136 teams for the
development phase, with 13 qualifying for the final round. The results
re-verified by the organizing team were used for the final ranking. This paper
comprehensively reviews the challenge, detailing the dataset introduction,
protocol definition, evaluation criteria, and a summary of published results.
Finally, we focus on the detailed analysis of the highest-performing algorithms
and offer potential directions for unified physical-digital attack detection
inspired by this competition. Challenge Website:
this https URL

面部防伪（FAS）是用于保护面部识别系统（FR）的关键。在实际场景中，FR 系统面临着物理和数字攻击。然而，现有算法通常只针对一种类型的攻击，这在 FR 系统面临混合物理数字威胁的实际情况下存在重要局限性。为促进统一攻击检测（UAD）算法的研究，我们收集了一个大规模 UniAttackData 数据集。UniAttackData 是最大的面向统一攻击检测的公共数据集，共有 28,706 个视频，每个唯一身份包含所有先进的攻击类型。基于这个数据集，我们组织了统一物理数字面部攻击检测挑战赛，以促进统一攻击检测领域的研究。在开发阶段，吸引了 136 个团队参与，最终有 13 个团队晋级到决赛。最终排名使用了组织团队验证的结果。本文全面回顾了该挑战赛，详细介绍了数据集介绍、协议定义、评估标准以及已发表结果的总结。最后，我们重点分析了表现最好的算法，并提供了受这次比赛启发的统一物理数字攻击检测的潜在方向。

统一的物理数字攻击检测挑战

Unified Physical-Digital Attack Detection Challenge

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。