  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.
