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Compact diffusion models can now leverage the power of high-capacity Teachers without architectural changes, achieving remarkable performance gains.
Existing deepfake detectors crumble when faced with realistic, multi-region speech inpainting, leaving a gaping vulnerability that this work begins to address.
Transformer-based detectors can significantly improve damage severity estimation by incorporating ordinal-aware supervision, even surpassing CNN-based models in ordinal consistency.
