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

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12357)


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


Face anti-spoofing Large-scale dataset 



This work is supported in part by SenseTime Group Limited, in part by National Science Foundation of China Grant No. U1934220 and 61790575, and the project “Safety data acquisition equipment for industrial enterprises No.134”. The corresponding author is Jing Shao. The contributions of Yuanhan Zhang and Zhenfei Yin are Equal.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Beijing Jiaotong UniversityBeijingChina
  2. 2.SenseTime Group Limited Hong KongChina
  3. 3.The Chinese University of Hong KongHong KongChina

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