  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.
