Quality-Aware Calibration for AI-Generated Image Detection in the Wild
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 ...
| Authors | Fabrizio Guillaro, Vincenzo De Rosa, Davide Cozzolino et al. |
|---|---|
| Primary category | cs.CV |
| Published | 16 April 2026 |
| Last updated at source | 16 April 2026 |
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