Quality-Aware Calibration for AI-Generated Image Detection in the Wild

• URL: http://arxiv.org/abs/2604.15027v1
• Date: Thu, 16 Apr 2026 13:56:52 GMT
• Title: Quality-Aware Calibration for AI-Generated Image Detection in the Wild
• Abstract summary: QuAD (Quality-Aware calibration with near-duplicates) is a novel framework that makes decisions based on all available near-duplicates of the same image.<n>We introduce two datasets: AncesTree, an in-lab dataset of 136k images organized in degradation trees that simulate online reposting dynamics, and ReWIND, a real-world dataset of nearly 10k near-duplicate images collected from viral web content.
• Score: 13.4681679159494
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Significant progress has been made in detecting synthetic images, however most existing approaches operate on a single image instance and overlook a key characteristic of real-world dissemination: as viral images circulate on the web, multiple near-duplicate versions appear and lose quality due to repeated operations like recompression, resizing and cropping. As a consequence, the same image may yield inconsistent forensic predictions based on which version has been analyzed. In this work, to address this issue we propose QuAD (Quality-Aware calibration with near-Duplicates) a novel framework that makes decisions based on all available near-duplicates of the same image. Given a query, we retrieve its online near-duplicates and feed them to a detector: the resulting scores are then aggregated based on the estimated quality of the corresponding instance. By doing so, we take advantage of all pieces of information while accounting for the reduced reliability of images impaired by multiple processing steps. To support large-scale evaluation, we introduce two datasets: AncesTree, an in-lab dataset of 136k images organized in stochastic degradation trees that simulate online reposting dynamics, and ReWIND, a real-world dataset of nearly 10k near-duplicate images collected from viral web content. Experiments on several state-of-the-art detectors show that our quality-aware fusion improves their performance consistently, with an average gain of around 8% in terms of balanced accuracy compared to plain average. Our results highlight the importance of jointly processing all the images available online to achieve reliable detection of AI-generated content in real-world applications. Code and data are publicly available at https://grip-unina.github.io/QuAD/

Related papers

• Text Over Image: Auditing Multimodal Robustness in Synthetic Medical Image Detection [38.146353416394234]
  We reformulate synthetic medical image detection as an audit of multimodal robustness at the image-record interface.<n>We find that changing the metadata context alone can flip authenticity judgments.<n>We propose an inference-time mitigation pipeline that detects and neutralizes provenance shortcuts without model retraining.
  arXiv  Detail & Related papers  (2026-06-24T04:08:41Z)
• GenShield: Unified Detection and Artifact Correction for AI-Generated Images [65.11434977803509]
  GenShield is a framework that performs AIGI detection and artifact correction in a closed loop from diagnosis to restoration.<n>A high-quality dataset with large-scale artifact-restored'' pairs is constructed alongside a unified evaluation pipeline.
  arXiv  Detail & Related papers  (2026-05-15T16:06:20Z)
• RealHD: A High-Quality Dataset for Robust Detection of State-of-the-Art AI-Generated Images [14.691738115737962]
  We propose a high-quality, large-scale dataset comprising over 730,000 images across multiple categories, including both real and AI-generated images.<n>The generated images are synthesized via state-of-the-art methods, including text-to-image generation (guided by over 10,000 carefully designed prompts), image inpainting, image refinement, and face swapping.<n>Our dataset not only serves as a strong benchmark for evaluating detection methods but also contributes to advancing the robustness of AI-generated image detection techniques.
  arXiv  Detail & Related papers  (2026-02-11T05:38:40Z)
• Provenance of AI-Generated Images: A Vector Similarity and Blockchain-based Approach [3.632189127068905]
  We propose an embedding-based AI image detection framework to distinguish AI-generated images from real (human-created) ones.<n>Our methodology is built on the hypothesis that AI-generated images demonstrate closer embedding proximity to other AI-generated content.<n>Our results confirm that moderate to high perturbations minimally impact the embedding signatures, with perturbed images maintaining close similarity matches to their original versions.
  arXiv  Detail & Related papers  (2025-10-15T00:49:56Z)
• Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios [54.07895223545793]
  This paper introduces the Real-World Robustness dataset (RRDataset) for comprehensive evaluation of detection models across three dimensions.<n>RRDataset includes high-quality images from seven major scenarios.<n>We benchmarked 17 detectors and 10 vision-language models (VLMs) on RRDataset and conducted a large-scale human study.
  arXiv  Detail & Related papers  (2025-09-11T06:15:52Z)
• CO-SPY: Combining Semantic and Pixel Features to Detect Synthetic Images by AI [58.35348718345307]
  Current efforts to distinguish between real and AI-generated images may lack generalization.<n>We propose a novel framework, Co-Spy, that first enhances existing semantic features.<n>We also create Co-Spy-Bench, a comprehensive dataset comprising 5 real image datasets and 22 state-of-the-art generative models.
  arXiv  Detail & Related papers  (2025-03-24T01:59:29Z)
• Zero-Shot Detection of AI-Generated Images [54.01282123570917]
  We propose a zero-shot entropy-based detector (ZED) to detect AI-generated images. Inspired by recent works on machine-generated text detection, our idea is to measure how surprising the image under analysis is compared to a model of real images. ZED achieves an average improvement of more than 3% over the SoTA in terms of accuracy.
  arXiv  Detail & Related papers  (2024-09-24T08:46:13Z)
• ImagiNet: A Multi-Content Benchmark for Synthetic Image Detection [0.0]
  We introduce ImagiNet, a dataset of 200K examples spanning four categories: photos, paintings, faces, and miscellaneous.<n>Synthetic images in ImagiNet are produced with both open-source and proprietary generators, whereas real counterparts for each content type are collected from public datasets.
  arXiv  Detail & Related papers  (2024-07-29T13:57:24Z)
• A Sanity Check for AI-generated Image Detection [49.08585395873425]
  We propose AIDE (AI-generated Image DEtector with Hybrid Features) to detect AI-generated images.<n>AIDE achieves +3.5% and +4.6% improvements to state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-06-27T17:59:49Z)
• Image Quality Assessment using Contrastive Learning [50.265638572116984]
  We train a deep Convolutional Neural Network (CNN) using a contrastive pairwise objective to solve the auxiliary problem. We show through extensive experiments that CONTRIQUE achieves competitive performance when compared to state-of-the-art NR image quality models. Our results suggest that powerful quality representations with perceptual relevance can be obtained without requiring large labeled subjective image quality datasets.
  arXiv  Detail & Related papers  (2021-10-25T21:01:00Z)

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.