ML Anthology

Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics

Yuezun Li, Xin Yang, Pu Sun, Honggang Qi, Siwei Lyu
CVPR 2020
doi:10.1109/CVPR42600.2020.00327 /cvpr/2020/li2020cvpr-celebdf/

Abstract

AI-synthesized face-swapping videos, commonly known as DeepFakes, is an emerging problem threatening the trustworthiness of online information. The need to develop and evaluate DeepFake detection algorithms calls for datasets of DeepFake videos. However, current DeepFake datasets suffer from low visual quality and do not resemble DeepFake videos circulated on the Internet. We present a new large-scale challenging DeepFake video dataset, Celeb-DF, which contains 5,639 high-quality DeepFake videos of celebrities generated using improved synthesis process. We conduct a comprehensive evaluation of DeepFake detection methods and datasets to demonstrate the escalated level of challenges posed by Celeb-DF.

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Cite

Text

Li et al. "Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020. doi:10.1109/CVPR42600.2020.00327

Markdown

[Li et al. "Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.](https://mlanthology.org/cvpr/2020/li2020cvpr-celebdf/) doi:10.1109/CVPR42600.2020.00327

BibTeX

@inproceedings{li2020cvpr-celebdf,
  title     = {{Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics}},
  author    = {Li, Yuezun and Yang, Xin and Sun, Pu and Qi, Honggang and Lyu, Siwei},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2020},
  doi       = {10.1109/CVPR42600.2020.00327},
  url       = {https://mlanthology.org/cvpr/2020/li2020cvpr-celebdf/}
}