CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations

TL;DR

This paper introduces CelebA-Spoof, a large and diverse face anti-spoofing dataset with rich annotations, enabling better training and evaluation of spoof detection methods in real-world scenarios.

Contribution

The creation of CelebA-Spoof, a large-scale, diverse, and richly annotated dataset for face anti-spoofing, addressing limitations of existing datasets and facilitating improved research.

Findings

Benchmarking reveals limitations of current methods.

Rich annotations enable multi-task learning.

Dataset supports diverse spoof attack scenarios.

Abstract

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.