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With recent advances in computer vision and graphics, it is now possible togenerate videos with extremely realistic synthetic faces, even in real time.Countless applications are possible, some of which raise a legitimate alarm,calling for reliable detectors of fake videos. In fact, distinguishing betweenoriginal and manipulated video can be a challenge for humans and computersalike, especially when the videos are compressed or have low resolution, as itoften happens on social networks. Research on the detection of facemanipulations has been seriously hampered by the lack of adequate datasets. Tothis end, we introduce a novel face manipulation dataset of about half amillion edited images (from over 1000 videos). The manipulations have beengenerated with a state-of-the-art face editing approach. It exceeds allexisting video manipulation datasets by at least an order of magnitude. Usingour new dataset, we introduce benchmarks for classical image forensic tasks,including classification and segmentation, considering videos compressed atvarious quality levels. In addition, we introduce a benchmark evaluation forcreating indistinguishable forgeries with known ground truth; for instance withgenerative refinement models.

FaceForensics: A Large-scale Video Dataset for Forgery Detection in Human Faces