{"ID":2825362,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.20937","arxiv_id":"2512.20937","title":"Beyond Artifacts: Real-Centric Envelope Modeling for Reliable AI-Generated Image Detection","abstract":"The rapid progress of generative models has intensified the need for reliable and robust detection under real-world conditions. However, existing detectors often overfit to generator-specific artifacts and remain highly sensitive to real-world degradations. As generative architectures evolve and images undergo multi-round cross-platform sharing and post-processing (chain degradations), these artifact cues become obsolete and harder to detect. To address this, we propose Real-centric Envelope Modeling (REM), a new paradigm that shifts detection from learning generator artifacts to modeling the robust distribution of real images. REM introduces feature-level perturbations in self-reconstruction to generate near-real samples, and employs an envelope estimator with cross-domain consistency to learn a boundary enclosing the real image manifold. We further build RealChain, a comprehensive benchmark covering both open-source and commercial generators with simulated real-world degradation. Across eight benchmark evaluations, REM achieves an average improvement of 7.5% over state-of-the-art methods, and notably maintains exceptional generalization on the severely degraded RealChain benchmark, establishing a solid foundation for synthetic image detection under real-world conditions. The code and the RealChain benchmark will be made publicly available upon acceptance of the paper.","short_abstract":"The rapid progress of generative models has intensified the need for reliable and robust detection under real-world conditions. However, existing detectors often overfit to generator-specific artifacts and remain highly sensitive to real-world degradations. As generative architectures evolve and images undergo multi-ro...","url_abs":"https://arxiv.org/abs/2512.20937","url_pdf":"https://arxiv.org/pdf/2512.20937v1","authors":"[\"Ruiqi Liu\",\"Yi Han\",\"Zhengbo Zhang\",\"Liwei Yao\",\"Zhiyuan Yan\",\"Jialiang Shen\",\"ZhiJin Chen\",\"Boyi Sun\",\"Lubin Weng\",\"Jing Dong\",\"Yan Wang\",\"Shu Wu\"]","published":"2025-12-24T04:41:04Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}