Less Prior, More Forensics: Reducing Pretrained Semantic Priors for Generalizable AI-Generated Image Detection

The growing realism of generative models has blurred the boundary between real and synthetic content, posing significant challenges to reliable AI-generated image detection. In this paper, we identify a failure pattern, termed semantic fallback, where semantic priors inherited from pretraining remain dominant in the representation space after forensic fine-tuning, thereby limiting the generalization of VFM-based detectors. Building on this insight, we propose a Geometric Semantic Decoupling (GSD) framework, which explicitly suppresses dominant pretrained geometry, thereby promoting invariant forensic representations. Specifically, GSD leverages a frozen CLIP encoder to estimate the dominant pretrained subspace via Singular Value Decomposition (SVD). It then suppresses the prior-aligned components through a geometry-constrained formulation with the suppression strength adaptively modulated across samples and layers. We further introduce a mini-batch SVD approximation strategy that amortizes subspace estimation, achieving over a 15× reduction in computational overhead while preserving effectiveness. Empirical results show that our method achieves state-of-the-art performance on various datasets, with average video-level AUCs of 94.5% (+2.6%) and 97.8% (+3.0%) on two face-forgery protocols, and average accuracies of 97.3% (+2.1%) on the synthetic-image benchmark. Our code is available at https://anonymous.4open.science/r/Null-Space_CLIP.