LOGER ensembles heterogeneous global vision models with selective local patch aggregation via multiple instance learning to achieve robust deepfake detection across varied manipulations and degradations.
Dˆ 3: scaling up deepfake detection by learning from discrepancy
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HEDGE is a heterogeneous ensemble using progressive DINOv3 training, multi-scale features, and MetaCLIP2 diversity with dual-gating fusion to achieve robust AI-generated image detection and 4th place in the NTIRE 2026 challenge.
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LOGER: Local--Global Ensemble for Robust Deepfake Detection in the Wild
LOGER ensembles heterogeneous global vision models with selective local patch aggregation via multiple instance learning to achieve robust deepfake detection across varied manipulations and degradations.
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HEDGE: Heterogeneous Ensemble for Detection of AI-GEnerated Images in the Wild
HEDGE is a heterogeneous ensemble using progressive DINOv3 training, multi-scale features, and MetaCLIP2 diversity with dual-gating fusion to achieve robust AI-generated image detection and 4th place in the NTIRE 2026 challenge.