Attention Consistency Refined Masked Frequency Forgery Representation
  for Generalizing Face Forgery Detection
Decheng Liu Tao Chen Chunlei Peng Nannan Wang Ruimin Hu Xinbo Gao
Due to the successful development of deep image generation technology, visual
data forgery detection would play a more important role in social and economic
security. Existing forgery detection methods suffer from unsatisfactory
generalization ability to determine the authenticity in the unseen domain. In
this paper, we propose a novel Attention Consistency Refined masked frequency
forgery representation model toward generalizing face forgery detection
algorithm (ACMF). Most forgery technologies always bring in high-frequency
aware cues, which make it easy to distinguish source authenticity but difficult
to generalize to unseen artifact types. The masked frequency forgery
representation module is designed to explore robust forgery cues by randomly
discarding high-frequency information. In addition, we find that the forgery
attention map inconsistency through the detection network could affect the
generalizability. Thus, the forgery attention consistency is introduced to
force detectors to focus on similar attention regions for better generalization
ability. Experiment results on several public face forgery datasets
(FaceForensic++, DFD, Celeb-DF, and WDF datasets) demonstrate the superior
performance of the proposed method compared with the state-of-the-art methods.