Self-Supervised AI-Generated Image Detection: A Camera Metadata Perspective
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
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| _version_ | 1866914357227552768 |
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| author | Zhong, Nan Zou, Mian Xu, Yiran Qian, Zhenxing Zhang, Xinpeng Wu, Baoyuan Ma, Kede |
| author_facet | Zhong, Nan Zou, Mian Xu, Yiran Qian, Zhenxing Zhang, Xinpeng Wu, Baoyuan Ma, Kede |
| contents | The proliferation of AI-generated imagery poses escalating challenges for multimedia forensics, yet many existing detectors depend on assumptions about the internals of specific generative models, limiting their cross-model applicability. We introduce a self-supervised approach for detecting AI-generated images that leverages camera metadata -- specifically exchangeable image file format (EXIF) tags -- to learn features intrinsic to digital photography. Our pretext task trains a feature extractor solely on camera-captured photographs by classifying categorical EXIF tags (\emph{e.g.}, camera model and scene type) and pairwise-ranking ordinal and continuous EXIF tags (\emph{e.g.}, focal length and aperture value). Using these EXIF-induced features, we first perform one-class detection by modeling the distribution of photographic images with a Gaussian mixture model and flagging low-likelihood samples as AI-generated. We then extend to binary detection that treats the learned extractor as a strong regularizer for a classifier of the same architecture, operating on high-frequency residuals from spatially scrambled patches. Extensive experiments across various generative models demonstrate that our EXIF-induced detectors substantially advance the state of the art, delivering strong generalization to in-the-wild samples and robustness to common benign image perturbations. The code and model are publicly available at https://github.com/Ekko-zn/SDAIE. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_05651 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Self-Supervised AI-Generated Image Detection: A Camera Metadata Perspective Zhong, Nan Zou, Mian Xu, Yiran Qian, Zhenxing Zhang, Xinpeng Wu, Baoyuan Ma, Kede Computer Vision and Pattern Recognition The proliferation of AI-generated imagery poses escalating challenges for multimedia forensics, yet many existing detectors depend on assumptions about the internals of specific generative models, limiting their cross-model applicability. We introduce a self-supervised approach for detecting AI-generated images that leverages camera metadata -- specifically exchangeable image file format (EXIF) tags -- to learn features intrinsic to digital photography. Our pretext task trains a feature extractor solely on camera-captured photographs by classifying categorical EXIF tags (\emph{e.g.}, camera model and scene type) and pairwise-ranking ordinal and continuous EXIF tags (\emph{e.g.}, focal length and aperture value). Using these EXIF-induced features, we first perform one-class detection by modeling the distribution of photographic images with a Gaussian mixture model and flagging low-likelihood samples as AI-generated. We then extend to binary detection that treats the learned extractor as a strong regularizer for a classifier of the same architecture, operating on high-frequency residuals from spatially scrambled patches. Extensive experiments across various generative models demonstrate that our EXIF-induced detectors substantially advance the state of the art, delivering strong generalization to in-the-wild samples and robustness to common benign image perturbations. The code and model are publicly available at https://github.com/Ekko-zn/SDAIE. |
| title | Self-Supervised AI-Generated Image Detection: A Camera Metadata Perspective |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.05651 |