{"ID":2832528,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.05651","arxiv_id":"2512.05651","title":"Self-Supervised AI-Generated Image Detection: A Camera Metadata Perspective","abstract":"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.","short_abstract":"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 le...","url_abs":"https://arxiv.org/abs/2512.05651","url_pdf":"https://arxiv.org/pdf/2512.05651v2","authors":"[\"Nan Zhong\",\"Mian Zou\",\"Yiran Xu\",\"Zhenxing Qian\",\"Xinpeng Zhang\",\"Baoyuan Wu\",\"Kede Ma\"]","published":"2025-12-05T11:53:18Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":606252,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2832528,"paper_url":"https://arxiv.org/abs/2512.05651","paper_title":"Self-Supervised AI-Generated Image Detection: A Camera Metadata Perspective","repo_url":"https://github.com/Ekko-zn/SDAIE","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}