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。