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 large-scale datasets. 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.
@article{arxiv.1909.12962,
title = {Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics},
author = {Yuezun Li and Xin Yang and Pu Sun and Honggang Qi and Siwei Lyu},
journal= {arXiv preprint arXiv:1909.12962},
year = {2020}
}