  As facial interaction systems are prevalently deployed, security and
reliability of these systems become a critical issue, with substantial research
efforts devoted. Among them, face anti-spoofing emerges as an important area,
whose objective is to identify whether a presented face is live or spoof.
Recently, a large-scale face anti-spoofing dataset, CelebA-Spoof which
comprised of 625,537 pictures of 10,177 subjects has been released. It is the
largest face anti-spoofing dataset in terms of the numbers of the data and the
subjects. This paper reports methods and results in the CelebA-Spoof Challenge
2020 on Face AntiSpoofing which employs the CelebA-Spoof dataset. The model
evaluation is conducted online on the hidden test set. A total of 134
participants registered for the competition, and 19 teams made valid
submissions. We will analyze the top ranked solutions and present some
discussion on future work directions.
