| Title | Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S. |
| Conference Name | 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |
| Date Published | jun |
| Keywords | AI-synthesized face-swapping videos, Celeb-DF, Decoding, DeepFake, DeepFake detection algorithms, DeepFake forensics, DeepFake video dataset, detection algorithms, Human Behavior, human factors, Image color analysis, Image forensics, Internet, Metrics, online information trustworthiness, pubcrawl, resilience, Resiliency, Scalability, Training, video signal processing, Videos, visualization, YouTube |
| 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. |
| DOI | 10.1109/CVPR42600.2020.00327 |
| Citation Key | li_celeb-df_2020 |
Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics