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：人脸反欺诈（FAS）对于保障人脸识别系统（FR）的安全至关重要。在实际场景中，人脸识别系统同时面临物理攻击与数字攻击的威胁。然而，现有算法通常仅能应对单一类型的攻击，这严重限制了其在混合物理-数字威胁场景下的应用效果。为促进统一攻击检测（UAD）算法研究，我们构建了大规模UniAttackData数据集，该数据集是目前最大的公开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，我们发起了统一物理-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。本次挑战赛吸引了136支队伍参与开发阶段，其中13支队伍晋级最终轮次。由组委会复核验证的结果作为最终排名依据。本文全面回顾了该挑战赛，详细介绍了数据集说明、协议定义、评估标准及已公布结果的总结。最后，我们聚焦分析了性能最优的算法，并基于本次竞赛提出了统一物理-数字攻击检测的潜在研究方向。挑战赛官网：https://sites.google.com/view/face-anti-spoofing-challenge/welcome/challengecvpr2024