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Kai-Yuan Deng

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Preprint Oct 2026

FADE: Frame-Aware Diffusion-Transformer-based Multi-Concept Erasure for Video Unlearning

Text-to-video (T2V) diffusion models can reproduce copyrighted, violent, or explicit content, which motivates concept erasure: removing designated concepts from a pretrained model while preserving its behavior on everything else. Existing T2V erasure methods leave two problems open. Their frame-agnostic suppression can...

Yu-Chen Li, Kai-Yuan Deng, Chaoran Feng et al. · 0 citations
#artificial intelligence Preprint Oct 2026

No Concept Escapes the Audit: Auditing-Aware Unlearning for Verifiable Concept Erasure in Diffusion Models

Text-to-image diffusion models can generate prohibited content, which motivates concept erasure through machine unlearning. Most erasure methods intervene at the text interface, through prompt modification or localized updates to text-conditioning weights, and they are evaluated by what the model outputs for given prom...

Kai-Yuan Deng, Yu-Chen Li, Gen Li et al. · 0 citations

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