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Face Anti-spoofing (FAS) is essential to secure face recognition systems fromvarious physical attacks. However, most of the studies lacked consideration oflong-distance scenarios. Specifically, compared with FAS in traditional scenessuch as phone unlocking, face payment, and self-service security inspection,FAS in long-distance such as station squares, parks, and self-servicesupermarkets are equally important, but it has not been sufficiently exploredyet. In order to fill this gap in the FAS community, we collect a large-scaleSurveillance High-Fidelity Mask (SuHiFiMask). SuHiFiMask contains $10,195$videos from $101$ subjects of different age groups, which are collected by $7$mainstream surveillance cameras. Based on this dataset and protocol-$3$ forevaluating the robustness of the algorithm under quality changes, we organizeda face presentation attack detection challenge in surveillance scenarios. Itattracted 180 teams for the development phase with a total of 37 teamsqualifying for the final round. The organization team re-verified and re-ranthe submitted code and used the results as the final ranking. In this paper, wepresent an overview of the challenge, including an introduction to the datasetused, the definition of the protocol, the evaluation metrics, and theannouncement of the competition results. Finally, we present the top-rankedalgorithms and the research ideas provided by the competition for attackdetection in long-range surveillance scenarios.

Surveillance Face Presentation Attack Detection Challenge