FAIR: Feature-Augmented Implicit Regularization for AI-generated Fake Image Detection

Generalization remains a critical bottleneck in AI-generatedimage detection. Because many modern generators are proprietary oradversarially modified, existing detectors overfit to the low-level tex-tural patterns of accessible training data, resulting in severe failureson unseen domains. Conventional regularization techniques (e.g., L1 /L2norms, Dropout) apply indiscriminate parametric constraints and fail toprovide the domain-invariant structure necessary for cross-generator ro-bustness. To address this, we propose Feature-Augmented Implicit Reg-ularization (FAIR). FAIR introduces an orthogonal, macro-structuralprior, specifically, Scene Composition Structure (SCS), during train-ing to geometrically constrain the model’s optimization trajectory. Byaugmenting the primary feature space with domain-invariant SCS fea-tures, FAIR explicitly penalizes texture-biased shortcut learning. Cru-cially, this structural prior is entirely discarded at inference, yielding asmoothed, generalized decision boundary with zero architectural or com-putational overhead. Extensive evaluations across five massive bench-marks demonstrate that integrating FAIR into state-of-the-art detectorssignificantly improves cross-generator generalization, boosting accuracyby up to 8.04% and establishing new state-of-the-art robustness in zero-shot transfer scenarios.