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Face anti-spoofing (FAS) is an essential mechanism for safeguarding theintegrity of automated face recognition systems. Despite substantialadvancements, the generalization of existing approaches to real-worldapplications remains challenging. This limitation can be attributed to thescarcity and lack of diversity in publicly available FAS datasets, which oftenleads to overfitting during training or saturation during testing. In terms ofquantity, the number of spoof subjects is a critical determinant. Most datasetscomprise fewer than 2,000 subjects. With regard to diversity, the majority ofdatasets consist of spoof samples collected in controlled environments usingrepetitive, mechanical processes. This data collection methodology results inhomogenized samples and a dearth of scenario diversity. To address theseshortcomings, we introduce the Wild Face Anti-Spoofing (WFAS) dataset, alarge-scale, diverse FAS dataset collected in unconstrained settings. Ourdataset encompasses 853,729 images of 321,751 spoof subjects and 529,571 imagesof 148,169 live subjects, representing a substantial increase in quantity.Moreover, our dataset incorporates spoof data obtained from the internet,spanning a wide array of scenarios and various commercial sensors, including 17presentation attacks (PAs) that encompass both 2D and 3D forms. This novel datacollection strategy markedly enhances FAS data diversity. Leveraging the WFASdataset and Protocol 1 (Known-Type), we host the Wild Face Anti-SpoofingChallenge at the CVPR2023 workshop. Additionally, we meticulously evaluaterepresentative methods using Protocol 1 and Protocol 2 (Unknown-Type). Throughan in-depth examination of the challenge outcomes and benchmark baselines, weprovide insightful analyses and propose potential avenues for future research.The dataset is released under Insightface.

Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results