Towards Generalizable and Robust Face Manipulation Detection via Bag-of-feature
Changtao Miao, Qi Chu, Weihai Li, Tao Gong, Wanyi Zhuang, Nenghai Yu
Abstract
Over the past several years, 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 ro-bustness by bag-of-feature. Specifically, we extend Transformers using bag-of-feature approach to encode inter-patch relation-ships, allowing it to learn forgery features without any additional mask supervision. Extensive experiments demonstrate that our method can outperform competing for state-of-the-art methods on FaceForensics++, Celeb-DF and DeeperForensics-l.0 datasets.