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: https://sites.google.com/view/face-anti-spoofing-challenge/welcome/challengecvpr2024.

翻译：人脸反欺骗（FAS）对于保障人脸识别（FR）系统安全至关重要。在现实场景中，FR系统同时面临物理攻击与数字攻击的威胁。然而，现有算法通常仅能处理单一类型的攻击，这一局限性在FR系统面临混合物理-数字威胁的真实场景中尤为突出。为促进统一攻击检测（UAD）算法的研究，我们构建了大规模的UniAttackData数据集。该数据集是当前公开最大的统一攻击检测数据集，包含28,706个视频，每个独立身份均涵盖所有高级攻击类型。基于该数据集，我们组织了"统一物理-数字人脸攻击检测挑战赛"，旨在推动统一攻击检测领域的研究。该竞赛吸引了136支队伍参与开发阶段，其中13支队伍晋级决赛。最终排名采用由组委会复核的结果。本文全面回顾本次挑战赛，详细阐述数据集介绍、协议定义、评估标准及已发布成果总结。最后，我们重点分析性能最优的算法，并基于本次竞赛提出统一物理-数字攻击检测的潜在研究方向。挑战赛网站：https://sites.google.com/view/face-anti-spoofing-challenge/welcome/challengecvpr2024