  Face Anti-spoofing (FAS) is essential to secure face recognition systems from
various physical attacks. However, recent research generally focuses on
short-distance applications (i.e., phone unlocking) while lacking consideration
of long-distance scenes (i.e., surveillance security checks). In order to
promote relevant research and fill this gap in the community, we collect a
large-scale Surveillance High-Fidelity Mask (SuHiFiMask) dataset captured under
40 surveillance scenes, which has 101 subjects from different age groups with
232 3D attacks (high-fidelity masks), 200 2D attacks (posters, portraits, and
screens), and 2 adversarial attacks. In this scene, low image resolution and
noise interference are new challenges faced in surveillance FAS. Together with
the SuHiFiMask dataset, we propose a Contrastive Quality-Invariance Learning
(CQIL) network to alleviate the performance degradation caused by image quality
from three aspects: (1) An Image Quality Variable module (IQV) is introduced to
recover image information associated with discrimination by combining the
super-resolution network. (2) Using generated sample pairs to simulate quality
variance distributions to help contrastive learning strategies obtain robust
feature representation under quality variation. (3) A Separate Quality Network
(SQN) is designed to learn discriminative features independent of image
quality. Finally, a large number of experiments verify the quality of the
SuHiFiMask dataset and the superiority of the proposed CQIL.
