  Recent developments in computer vision and machine learning have made it
possible to create realistic manipulated videos of human faces, raising the
issue of ensuring adequate protection against the malevolent effects unlocked
by such capabilities. In this paper we propose local image features that are
shared across manipulated regions are the key element for the automatic
detection of manipulated face images. We also design a lightweight architecture
with the correct structural biases for extracting such features and derive a
multitask training scheme that consistently outperforms image class supervision
alone. The trained networks achieve state-of-the-art results in the
FaceForensics++ dataset using significantly reduced number of parameters and
are shown to work well in detecting fully generated face images.
