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

Computer Vision and Pattern Recognition