Beyond Artifacts: Real-Centric Envelope Modeling for Reliable AI-Generated Image Detection
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918262722265088 |
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| author | Liu, Ruiqi Han, Yi Zhang, Zhengbo Yao, Liwei Yan, Zhiyuan Shen, Jialiang Chen, ZhiJin Sun, Boyi Weng, Lubin Dong, Jing Wang, Yan Wu, Shu |
| author_facet | Liu, Ruiqi Han, Yi Zhang, Zhengbo Yao, Liwei Yan, Zhiyuan Shen, Jialiang Chen, ZhiJin Sun, Boyi Weng, Lubin Dong, Jing Wang, Yan Wu, Shu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_20937 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond Artifacts: Real-Centric Envelope Modeling for Reliable AI-Generated Image Detection Liu, Ruiqi Han, Yi Zhang, Zhengbo Yao, Liwei Yan, Zhiyuan Shen, Jialiang Chen, ZhiJin Sun, Boyi Weng, Lubin Dong, Jing Wang, Yan Wu, Shu Computer Vision and Pattern Recognition 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. |
| title | Beyond Artifacts: Real-Centric Envelope Modeling for Reliable AI-Generated Image Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.20937 |