Smoothing the Semantic Landscape: Generalizable AI-Generated Image Detection via Text-Induced Flatness

Recent advances in generative models have exposed a fundamental weakness of image fake detectors: they often fail to generalize to unseen synthesis methods. We attribute this failure to an optimization pathology in which detectors overfit to source-coupled artifacts and converge to sharp solutions, resulting in unstable decision boundaries under generator shifts. To address this issue, we propose a geometric optimization framework that treats diverse textual prompts as structured semantic perturbations in the joint image--text space. To ensure that these perturbations provide reliable supervision, we introduce an entropy-based anchor selection mechanism that discards ambiguous prompt conditions and retains semantically compatible anchors for each input. Based on these anchors, we further impose two complementary regularizations: (i) perturbation consistency regularization, which enforces stable predictions across nearby semantic conditions and encourages local smoothness of the decision function; and (ii) manifold expansion regularization, which preserves feature diversity in the residual space and alleviates representation collapse. Together, these components steer optimization toward flatter and more transferable solutions. Extensive experiments on multiple benchmarks show that our method substantially improves cross-generator generalization, outperforming previous state-of-the-art methods by 5.65% in average accuracy.