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There are concerns that new approaches to the synthesis of high quality facevideos may be misused to manipulate videos with malicious intent. The researchcommunity therefore developed methods for the detection of modified footage andassembled benchmark datasets for this task. In this paper, we examine how theperformance of forgery detectors depends on the presence of artefacts that thehuman eye can see. We introduce a new benchmark dataset for face video forgerydetection, of unprecedented quality. It allows us to demonstrate that existingdetection techniques have difficulties detecting fakes that reliably fool thehuman eye. We thus introduce a new family of detectors that examinecombinations of spatial and temporal features and outperform existingapproaches both in terms of detection accuracy and generalization.

VideoForensicsHQ: Detecting High-quality Manipulated Face Videos