Beyond the Prior Forgery Knowledge: Mining Critical Clues for General Face Forgery Detection. (arXiv:2304.12489v1 [cs.CV])

Face forgery detection is essential in combating malicious digital face
attacks. Previous methods mainly rely on prior expert knowledge to capture
specific forgery clues, such as noise patterns, blending boundaries, and
frequency artifacts. However, these methods tend to get trapped in local
optima, resulting in limited robustness and generalization capability. To
address these issues, we propose a novel Critical Forgery Mining (CFM)
framework, which can be flexibly assembled with various backbones to boost
their generalization and robustness performance. Specifically, we …

address artifacts attacks beyond build capture cfm critical detection digital expert forgery framework general knowledge local malicious mining noise novel patterns performance robustness