  Face Anti-spoofing (FAS) is essential to secure face recognition systems from
various physical attacks. However, most of the studies lacked consideration of
long-distance scenarios. Specifically, compared with FAS in traditional scenes
such as phone unlocking, face payment, and self-service security inspection,
FAS in long-distance such as station squares, parks, and self-service
supermarkets are equally important, but it has not been sufficiently explored
yet. In order to fill this gap in the FAS community, we collect a large-scale
Surveillance 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-$3$ for
evaluating the robustness of the algorithm under quality changes, we organized
a face presentation attack detection challenge in surveillance scenarios. It
attracted 180 teams for the development phase with a total of 37 teams
qualifying for the final round. The organization team re-verified and re-ran
the submitted code and used the results as the final ranking. In this paper, we
present an overview of the challenge, including an introduction to the dataset
used, the definition of the protocol, the evaluation metrics, and the
announcement of the competition results. Finally, we present the top-ranked
algorithms and the research ideas provided by the competition for attack
detection in long-range surveillance scenarios.
