Beyond Artifacts: Real-Centric Envelope Modeling for Reliable AI-Generated Image Detection

The rapid progress of generative models has intensix001Ced theneed for reliable and robust detection under real-world conditions. How-ever, existing detectors often overx001Ct to generator-specix001Cc artifacts andremain highly sensitive to real-world degradations. As generative archi-tectures evolve and images undergo multi-round cross-platform shar-ing and post-processing (chain degradations), these artifact cues be-come obsolete and harder to detect. To address this, we propose Real-centric Envelope Modeling (REM), a new paradigm that shifts de-tection from learning generator artifacts to modeling the robust distribu-tion of real images. REM introduces feature-level perturbations in self-reconstruction to generate near-real samples, and employs an envelopeestimator with cross-domain consistency to learn a boundary enclosingthe real image manifold. We further build RealChain , a comprehensivebenchmark covering both open-source and commercial generators withsimulated real-world degradation. REM achieves an average improve-ment of 6.9% over state-of-the-art (SOTA) methods across 12 bench-mark evaluations. Notably, it maintains exceptional generalization on theseverely degraded RealChain benchmark, outperforming existing meth-ods by 18.4%. These results establish a solid foundation for reliable syn-thetic image detection under challenging real-world conditions. The codeand datasets are available at https://github.com/handsome-rich/REM.