{
  "id": 151,
  "url": "https://arxiv.org/abs/2607.06432v1",
  "title": "TILDE: TILt-based Distributional Erasure for Concept Unlearning",
  "summary": "Concept unlearning in text-to-image diffusion models is critical for safe and practical deployment: with rising privacy concerns, copyright disputes, trademark constraints, and safety regulations, deployed systems must be able to suppress unwanted concepts after training. Existing methods often remove the target concept effectively, but practical unlearning also requires an equally fundamental property: the unlearned model should retain quality, diversity, and semantic coverage on benign generat",
  "authors": "Naveen George, Naoki Murata, Yuhta Takida, Konda Reddy Mopuri, Yuki Mitsufuji",
  "category": "research",
  "topics": "regulation,privacy-surveillance,copyright-ip",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-07T15:59:59.000Z",
  "fetched_at": "2026-07-14T14:14:19.969Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/151",
  "original_url": "https://arxiv.org/abs/2607.06432v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}