Where Detectors Fail: Probing Generative Space for Generalizable AI-Generated Image Detection
Detecting AI-generated images (AIGI) remains challenging because detectors often fail to generalize to unseen generators. Although existing methods are trained on large datasets, their performance still degrades when generation settings change, indicating that data scale alone is insufficient and that limited coverage of generative variations during training is a key factor. Studies on generative model editing show that small changes in internal representations can produce diverse and meaningful image variations, many of which are not explored under standard sampling. Leveraging this insight, we propose PROBE (Probing Robustness via Boundary Exploration), a framework that improves detecto...
| Authors | Zijie Cao, Weijie Tu, Yao Xiao et al. |
|---|---|
| Primary category | cs.CV |
| Published | 24 May 2026 |
| Last updated at source | 27 May 2026 |
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