  Over the past several years, in order to solve the problem of malicious abuse
of facial manipulation technology, face manipulation detection technology has
obtained considerable attention and achieved remarkable progress. However, most
existing methods have very impoverished generalization ability and robustness.
In this paper, we propose a novel method for face manipulation detection, which
can improve the generalization ability and robustness by bag-of-local-feature.
Specifically, we extend Transformers using bag-of-feature approach to encode
inter-patch relationships, allowing it to learn local forgery features without
any explicit supervision. Extensive experiments demonstrate that our method can
outperform competing state-of-the-art methods on FaceForensics++, Celeb-DF and
DeeperForensics-1.0 datasets.

Computer Vision and Pattern Recognition