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

• URL: http://arxiv.org/abs/2512.05651v1
• Date: Fri, 05 Dec 2025 11:53:18 GMT
• Title: Self-Supervised AI-Generated Image Detection: A Camera Metadata Perspective
• Authors: Nan Zhong, Mian Zou, Yiran Xu, Zhenxing Qian, Xinpeng Zhang, Baoyuan Wu, Kede Ma,
• Abstract summary: We introduce a self-supervised approach for detecting AI-generated images that leverages camera metadata.<n>We train a feature extractor solely on camera-captured photographs by classifying categorical EXIF tags.<n>Our detectors deliver strong generalization to in-the-wild samples and robustness to common benign image perturbations.
• Score: 80.10217707456046
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 (\eg, camera model and scene type) and pairwise-ranking ordinal and continuous EXIF tags (\eg, 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.

Related papers

• Unveiling Perceptual Artifacts: A Fine-Grained Benchmark for Interpretable AI-Generated Image Detection [95.08316274158165]
  X-AIGD provides pixel-level, categorized annotations of perceptual artifacts, spanning low-level distortions, high-level semantics, and cognitive-level counterfactuals.<n>Existing AIGI detectors demonstrate negligible reliance on perceptual artifacts, even at the most basic distortion level.<n>Explicitly aligning model attention with artifact regions can increase the interpretability and generalization of detectors.
  arXiv  Detail & Related papers  (2026-01-27T10:09:17Z)
• Detecting AI-Generated Images via Distributional Deviations from Real Images [6.615773227400183]
  We propose a Masking-based Pre-trained model Fine-Tuning (MPFT) strategy, which introduces a Texture-Aware Masking (TAM) mechanism to mask textured areas containing generative model-specific patterns during fine-tuning.<n>Our method, fine-tuned with only a minimal number of images, significantly outperforms existing approaches, achieving up to 98.2% and 94.6% average accuracy on the two datasets, respectively.
  arXiv  Detail & Related papers  (2026-01-07T05:00:13Z)
• $\bf{D^3}$QE: Learning Discrete Distribution Discrepancy-aware Quantization Error for Autoregressive-Generated Image Detection [85.9202830503973]
  Visual autoregressive (AR) models generate images through discrete token prediction.<n>We propose to leverage Discrete Distribution Discrepancy-aware Quantization Error (D$3$QE) for autoregressive-generated image detection.
  arXiv  Detail & Related papers  (2025-10-07T13:02:27Z)
• Bi-Level Optimization for Self-Supervised AI-Generated Face Detection [56.57881725223548]
  We introduce a self-supervised method for AI-generated face detectors based on bi-level optimization.<n>Our detectors significantly outperform existing approaches in both one-class and binary classification settings.
  arXiv  Detail & Related papers  (2025-07-30T16:38:29Z)
• Generalizable AI-Generated Image Detection Based on Fractal Self-Similarity in the Spectrum [38.302088844940556]
  We propose a novel detection method based on the fractal self-similarity of the spectrum.<n>We show that AI-generated images exhibit fractal-like spectral growth through periodic extension and low-pass filtering.<n>Our method mitigates the impact of varying spectral characteristics across different generators, improving detection performance for images from unseen models.
  arXiv  Detail & Related papers  (2025-03-11T14:37:06Z)
• Few-Shot Learner Generalizes Across AI-Generated Image Detection [14.069833211684715]
  Few-Shot Detector (FSD) is a novel AI-generated image detector which learns a specialized metric space for effectively distinguishing unseen fake images.<n>Experiments show FSD achieves state-of-the-art performance by $+11.6%$ average accuracy on the GenImage dataset with only $10$ additional samples.
  arXiv  Detail & Related papers  (2025-01-15T12:33:11Z)
• Epistemic Uncertainty for Generated Image Detection [107.62647907393377]
  We introduce a novel framework for AI-generated image detection through epistemic uncertainty, aiming to address critical security concerns in the era of generative models.<n>Our key insight stems from the observation that distributional discrepancies between training and testing data manifest distinctively in the epistemic uncertainty space of machine learning models.
  arXiv  Detail & Related papers  (2024-12-08T11:32:25Z)
• Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models [68.90917438865078]
  Deepfake techniques for facial synthesis and editing pose serious risks for generative models.<n>In this paper, we investigate how detection performance varies across model backbones, types, and datasets.<n>We introduce Contrastive Blur, which enhances performance on facial images, and MINDER, which addresses noise type bias, balancing performance across domains.
  arXiv  Detail & Related papers  (2024-11-28T13:04:45Z)
• Improving Interpretability and Robustness for the Detection of AI-Generated Images [6.116075037154215]
  We analyze existing state-of-the-art AIGI detection methods based on frozen CLIP embeddings. We show how to interpret them, shedding light on how images produced by various AI generators differ from real ones.
  arXiv  Detail & Related papers  (2024-06-21T10:33:09Z)
• RIGID: A Training-free and Model-Agnostic Framework for Robust AI-Generated Image Detection [60.960988614701414]
  RIGID is a training-free and model-agnostic method for robust AI-generated image detection. RIGID significantly outperforms existing trainingbased and training-free detectors.
  arXiv  Detail & Related papers  (2024-05-30T14:49:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.