CelebA-Spoof is a large-scale face anti-spoofing dataset that has 625,537 images from 10,177 subjects, which includes 43 rich attributes on face, illumination, environment and spoof types. Live images are selected from the CelebA dataset. We collect and annotate spoof images for CelebA-Spoof. Among 43 rich attributes, 40 attributes belong to live images including all facial components and accessories such as skin, nose, eyes, eyebrows, lip, hair, hat, eyeglass. 3 attributes belong to spoof images including spoof types, environments and illumination conditions.

CelebA-Spoof can be used to train and evaluate algorithms of face anti-spoofing, face presentation attacks, and robustness/security research.

Sample Images

Besides the annotation of Live/Spoof, Existing face anti-spoofing only annotate the spoof type. To further comprehensively investigate face anti-spoofing tasks from various perspectives, in CelebA-Spoof, we annotate 43 different annotations. 40 types of Face Attribute defined in CelebA plus 3 attributes of face anti-spoofing, including Spoof Type, Illumination Condition, and Environment.


For more details of the dataset, please refer to the paper "CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations".


  • The CelebA-Spoof dataset is available for non-commercial research purposes only.
  • You agree not to reproduce, duplicate, copy, sell, trade, resell or exploit for any commercial purposes, any portion of the images and any portion of derived data.
  • You agree not to further copy, publish or distribute any portion of the CelebA-Spoof dataset. Except, for internal use at a single site within the same organization it is allowed to make copies of the dataset.


  title = {CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations},
  author = {Zhang, Yuanhan and Yin, Zhenfei and Li, Yidong and Yin, Guojun and Yan, Junjie and Shao, Jing and Liu, Ziwei},
  booktitle = {European Conference on Computer Vision (ECCV)},
  year = {2020}


Please contact Yuanhan Zhang for questions about the dataset.