SCORE: Semantic-Conditioned Orthogonal Refinement for Generalizable AI-Generated Image Detection

Generalizable forgery detection requires forensic evidence that remains reliable under shifts in content, acquisition conditions, and generative mechanisms. A central challenge is that the diagnostic meaning of many forensic cues depends on the visual context in which they occur: local texture, boundary, and frequency responses can vary with identity, pose, expression, scene structure, and image quality. This motivates a controlled use of semantic context when forming and refining detection decisions. We propose SCORE, a Semantic-Conditioned Orthogonal Refinement framework that organizes detection into a semantic-conditioned primary stage and a conservative refinement stage. SCORE first uses the current visual state to parameterize a gated residual and forms an independently supervised primary prediction. It then constructs a sample-conditioned low-dimensional basis and applies soft orthogonal attenuation to obtain a structured refinement view that preserves part of the basis-aligned information. A conditional refinement head operates on this detached representation, while uncertainty derived from the detached primary prediction controls the strength of a bounded logit correction. SCORE achieves an average video AUC of $0.8984$ across five cross-dataset targets, an average video AUC of $0.9723$ over eight unseen manipulations in DF40, and $99.70\%$ mAP and $95.48\%$ mAcc across 19 UniversalFakeDetect (UFD) test sources. These results support structured semantic conditioning and controlled refinement as an effective approach to forgery detection under diverse distribution shifts.