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.

翻译：随着面部互动系统的普遍部署,这些系统的安全性和可靠性成为一个关键问题,并投入了大量的研究努力,其中,面部反威胁作为一个重要领域出现,目的是确定面部是否真实。最近,一个大规模面部反威胁数据集(CelebA-Spooof,由625 537张10 177个主题的相片组成的CelebA-Spoof)公布,这是最大的面部反威胁数据集,从数据数量和主题来看,这是最大的面部反威胁数据集。本文报告了使用西列巴-Spooof数据集的2020年脸部反威胁(CelebA-Spoof Challenge 2020)的方法和结果。模型评价在隐藏的测试集上进行在线进行。共有134名登记参加竞争,19个小组提交了有效文件。我们将分析排名最高的解决方案,并就未来工作方向提出一些讨论。