Automated In-the-Wild Data Collection for Continual AI Generated Image Detection
The rapid advancement of generative Artificial Intelligence (AI) has introduced significant challenges for reliable AI-generated image detection. Existing detectors often suffer from performance degradation under distribution shifts and when encountering newly emerging generative models. In this work, we propose a data-centric continual adaptation framework for updating detectors in evolving environments. We show that both in-the-wild data and generator-driven data are essential for adapting detectors. We introduce an automated, weakly supervised pipeline for constructing in-the-wild datasets through fact-check article retrieval. Additionally, we demonstrate that incorporating even a smal...
| Authors | Thanasis Pantsios, Dimitrios Karageorgiou, Christos Koutlis et al. |
|---|---|
| Primary category | cs.CV |
| Published | 4 May 2026 |
| Last updated at source | 4 May 2026 |
Models and datasets referencing this paper
- pthan12/AIGenImages2026Dataset
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