Unified Physical–Digital Face Attack Detection Challenge: A Review

Face antispoofing (FAS) technologies play a pivotal role in safeguarding face recognition (FR) systems against potential security loopholes. The biometric community has witnessed significant advancements lately, largely due to the exceptional performance of deep learning architectures and the abundance of substantial datasets. Despite these progress, FR systems remain susceptible to both physical and digital forgery attacks. However, most existing detection methods do not address both types of threats concurrently. To bridge this gap and foster the development of a comprehensive detection framework, we have compiled a unified dataset named UniAttackData. This dataset incorporates both physical and digital spoofing attacks while maintaining identity consistency, encompassing 1800 participants each subjected to two different physical attacks (PAs) and 12 different digital attacks (DAs), respectively. This effort has resulted in a comprehensive collection of 29,706 video samples. We organized the Chalearn FAS face attack detection challenge based on this novel resource to boost research aiming to promote joint antispoofing efforts. The Chalearn unified antispoofing attack detection challenge drew 136 teams during the development phase, with 13 teams advancing to the final round. The organizing team revalidated and re‐executed the submitted code to determine the final rankings. This paper provides a summary of the challenge, covering the dataset used, the protocol definition, the evaluation metrics, and the competition results. Additionally, we discuss the top‐ranked algorithms and the research insights offered by the competition for attack detection.