  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.
