AI RESEARCH
FiSeR: Fine-Grained Source Representations for Cross-Domain AI Image Detection
arXiv CS.CV
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ArXi:2606.00606v1 Announce Type: new Real-world synthetic image detectors often generalize poorly under domain shift despite strong in-domain performance. Using unsupervised UMAP projections, we find that natural and synthetic features remain partially separable on unseen datasets, yet performance still drops, suggesting that the classification head overfits to