From Specificity to Generality Revisiting Artifacts in Detecting Face

Detecting deepfakes has been an increasingly important topic, especially given the rapid development of AI generation techniques. In this paper, we ask: How can we build a universal detection framework that is effective for most facial deepfakes? One significant challenge is the wide diversity of existing deepfake generators, which produced varied types of forgery artifacts (e.g., lighting inconsistency, color mismatch, etc). But should we "teach" the detector to learn all these artifacts separately? It is impossible and impractical to elaborate on them all.