  The emergence of deepfake technologies has become a matter of social concern
as they pose threats to individual privacy and public security. It is now of
great significance to develop reliable deepfake detectors. However, with
numerous face manipulation algorithms present, it is almost impossible to
collect sufficient representative fake faces, and it is hard for existing
detectors to generalize to all types of manipulation. Therefore, we turn to
learn the distribution of real faces, and indirectly identify fake images that
deviate from the real face distribution. In this study, we propose Real Face
Foundation Representation Learning (RFFR), which aims to learn a general
representation from large-scale real face datasets and detect potential
artifacts outside the distribution of RFFR. Specifically, we train a model on
real face datasets by masked image modeling (MIM), which results in a
discrepancy between input faces and the reconstructed ones when applying the
model on fake samples. This discrepancy reveals the low-level artifacts not
contained in RFFR, making it easier to build a deepfake detector sensitive to
all kinds of potential artifacts outside the distribution of RFFR. Extensive
experiments demonstrate that our method brings about better generalization
performance, as it significantly outperforms the state-of-the-art methods in
cross-manipulation evaluations, and has the potential to further improve by
introducing extra real faces for training RFFR.
