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By decoupling detection from explanation, PATE-Forensics achieves unprecedented accuracy in deepfake forensics, setting a new benchmark for explainability in AI.
Decision-level fusion outperforms traditional feature-level methods in detecting audio-visual forgeries, revealing a critical flaw in existing assumptions about modality correspondence.
OKR achieves a remarkable +6.5% mAP improvement over existing methods by preventing knowledge interference in domain-incremental object detection.
