FiSeR uses coarse contrastive separation of natural vs synthetic images plus fine contrastive grouping by generator identity to improve cross-domain AUROC by +10.22 over DIRE baseline on multiple test sets.
When trained on Community, the overall performance of all methods is generally lower than that of training on WildFake, and most baselines degrade more substantially
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FiSeR: Fine-Grained Source Representations for Cross-Domain AI Image Detection
FiSeR uses coarse contrastive separation of natural vs synthetic images plus fine contrastive grouping by generator identity to improve cross-domain AUROC by +10.22 over DIRE baseline on multiple test sets.