Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics
This provides a more realistic benchmark for developing DeepFake detection algorithms, addressing a critical need in combating online misinformation.
The authors tackled the problem of low-quality DeepFake datasets by creating Celeb-DF, a large-scale dataset with 5,639 high-quality videos, and showed it poses greater challenges for detection methods.
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.