Self-Supervised AI-Generated Image Detection: A Camera Metadata Perspective

Nan Zhong; Mian Zou; Yiran Xu; Zhenxing Qian; Xinpeng Zhang; Baoyuan Wu; Kede Ma

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arXiv · 2512.05651

Self-Supervised AI-Generated Image Detection: A Camera Metadata Perspective

Nan Zhong·Mian Zou·Yiran Xu·Zhenxing Qian·Xinpeng Zhang·Baoyuan Wu·Kede Ma

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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.

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BibTeX RIS

Nan Zhong, Mian Zou, Yiran Xu, Zhenxing Qian, Xinpeng Zhang, Baoyuan Wu, Kede Ma. 2026-02-27. Self-Supervised AI-Generated Image Detection: A Camera Metadata Perspective. https://arxiv.org/abs/2512.05651

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