UniAttackData+: Unified Physical-Digital Attack Detection+ Challenge

Unified physical-digital face attack detection aims to develop a universal model capable of simultaneously detecting both digital and physical spoofing attacks. However, ex-isting training datasets generally lack comprehensive coverage of diverse attack types, which limits the generalization ability of detection models and hinders their effectiveness in real-world applications. To address this limitation, we released a significantly expanded dataset, UniAttack-Data+, at the 6th Face Anti-Spoofing Workshop @ICCV 2025. The dataset includes 2,875 participants from three major demographic groups–Africa, East Asia, and Central Asia-and contains 18,250 real videos collected under di-verse lighting conditions, backgrounds, and acquisition devices. For each participant, 54 types of attacks were simulated, comprising 14 physical and 40 digital attack variants, resulting in a total of 679,097 high-quality forged videos. Based on this dataset, we organized a Unified Attack Detection Challenge, which attracted 137 participating teams, with 12 teams advancing to the final round. The final rankings were determined based on results re-verified by the organizers. This paper reviews the challenge by introducing the dataset construction, protocol definitions, evaluation metrics, and competition results, and analyzes the top-performing algorithmic solutions, while also outlining future directions for unified physical-digital attack detection. Challenge Website: https://sites.google.com/view/face-anti-spoofing-challenge/welcome/challengeiccv2025?authuser=0