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. Though promising progress has been achieved, existing works still have diﬃculty in handling complex spoof attacks and generalizing to real-world scenarios. The main reason is that current face anti-spooﬁng datasets are limited in both quantity and diversity. To overcome these obstacles, we contribute a large-scale face anti-spooﬁng dataset, CelebA-Spoof, with the following appealing properties: 1) Quantity: CelebA-Spoof comprises of 625,537 pictures of 10,177 subjects, signiﬁcantly larger than the existing datasets. 2) Diversity: The spoof images are captured from 8 scenes (2 environments * 4 illumination conditions) with more than 10 sensors. 3) Annotation Richness: CelebA-Spoof contains 10 spoof type annotations, as well as the 40 attribute annotations inherited from the original CelebA dataset. Equipped with CelebA-Spoof, we carefully benchmark existing methods in a uniﬁed multi-task framework, Auxiliary Information Embedding Network (AENet), and reveal several valuable observations. Our key insight is that, compared with the commonly-used binary supervision or mid-level geometric representations, rich semantic annotations as auxiliary tasks can greatly boost the performance and generalizability of face anti-spooﬁng across a wide range of spoof attacks. Through comprehensive studies, we show that CelebA-Spoof serves as an eﬀective training data source. Models trained on CelebA-Spoof (without ﬁne-tuning) exhibit state-of-the-art performance on standard benchmarks such as CASIA-MFSD. The datasets are available at https://github.com/Davidzhangyuanhan/CelebA-Spoof .