arXiv:2607. 23492v1 Announce Type: cross Abstract: Concept erasure techniques (CETs) edit text-to-image diffusion models to erase undesired targets such as NSFW content or copyrighted styles, while preserving model utility on benign concepts.
By Shaswati Saha, Rajasekhar Anguluri, Manas Gaur
arXiv:2610.01969v1 Announce Type: new
Abstract: Concept erasure aims to remove a target concept, such as a copyrighted style, a recognizable character, or unsafe content, from a pretrained text-to-im...
By Yongliang Wu, Haori Lu, Yulun Wu, Jinqi Luo, Xingyu Zhu, Yaoyao Liu
The paper introduces CEASE, a training‑free method for continual concept erasure in text‑to‑image diffusion models. CEASE imposes two subspace constraints on a closed‑form solver to prevent interference across successive erasures, ensuring that new targets can be removed without undoing previously erased concepts. Experiments on erasing celebrities, artistic styles, and specific instances show that CEASE consistently balances erasure and preservation better than existing methods, which either degrade general generation or fail to fully erase targets.
By Yongliang Wu, Haori Lu, Jinqi Luo, Wei Cao, Xingyu Zhu, Yaoyao Liu
arXiv:2605.25765v2 Announce Type: replace-cross
Abstract: Existing closed-form methods for concept unlearning in text-to-image diffusion models typically derive editing directions from fixed text emb...
By Saemi Moon, Suhyeon Jun, Seoyeon Lee, Dongwoo Kim
CleanVideo introduces a selective erasure framework for text-to-video diffusion models, addressing the challenge of removing undesired visual concepts from videos. The method uses a low-dimensional subspace intervention guided by a tri-modal gating mechanism that jointly considers spatiotemporal visual features, timestep signals, and textual semantics to decide where, when, and whether to intervene. Experiments on three video diffusion models demonstrate that CleanVideo effectively erases target concepts while preserving visual fidelity, temporal coherence, and outperforming existing baselines in both frame-level and video-level evaluations, even under concept-recovery attacks.
By Junchi Liao, Hongji Li, Wenrui Zhou, Lijie Hu
arXiv:2512. 02657v2 Announce Type: replace-cross Abstract: Real-world deployment of text-to-image diffusion models requires continual concept removal as new privacy, copyright, or safety obligations arise over time.
By Naveen George, Naoki Murata, Yuhta Takida, Konda Reddy Mopuri, Yuki Mitsufuji