Prototype-Guided Tangent–Boundary Perturbation for AI-Generated Image Detection

Detectors that synthesize features during training have improved AI-generated image detection. However, our visualization reveals that synthesized features reshape rather than enrich the original fake-feature distribution, causing the detector to learn from the synthesized features while losing its understanding of the original distribution. To introduce additional training variations while retaining supervision on the original features, we propose Prototype-Guided Tangent-Boundary Perturbation (PTBP), a feature-level perturbation method. First, its orthogonal subspace decomposition uses class prototypes and within-class feature variances to decompose sampled Gaussian noise into components orthogonal and parallel to the discriminative direction. This decomposition allows PTBP to control what each perturbation component preserves. Then, controlled feature perturbation uses the orthogonal component to construct tangent views that preserve the predictions of the corresponding original features. It further adds a class-aware and margin-calibrated parallel component to construct boundary views, which are supervised with the original binary labels after class-aware shifts. The two views are trained using tangent consistency and boundary supervision, respectively. Both objectives are jointly optimized to encourage the detector to learn from additional training variations while retaining supervision on the original features. With only one training epoch, PTBP achieves the best performance in both cross-generator and compression-perturbed scenarios.