arXiv Computer Vision
Comprehensive Assessment and Analysis for NSFW Content Erasure in Text-to-Image Diffusion Models
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arXiv Computer Vision
Aug 31
Comprehensive Evaluation and Analysis for NSFW Concept Erasure in Text-to-Image Diffusion Models
The paper presents a comprehensive toolkit for evaluating and analyzing NSFW concept erasure in text-to-image diffusion models. It conducts the first systematic study of existing erasure methods, exploring how their mechanisms interact with empirical results. The authors offer detailed insights and practical guidance to improve content safety in diffusion models across real-world scenarios.
arXiv AI
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Erased but Not Forgotten: How Backdoors Compromise Concept Erasure
arXiv:2504. 21072v2 Announce Type: replace-cross Abstract: The expansion of text-to-image diffusion models has raised concerns about harmful outputs, from fabricated depictions of public figures to sexually explicit imagery.
arXiv Machine Learning
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To Erase, or Not to Erase: Robust Training-Free Concept Erasure with Preservation aware Adaptive Ranked Subspace Expansion
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.
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RASteer: Retain-Aware Activation Steering for Concept Erasure in Diffusion Models
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Hugging Face Trending Papers
Jul 6
Erasing Without Collateral Damage: Precise Concept Removal in Diffusion Models
Training-free concept erasure is an attractive mechanism for controlling text-to-image diffusion models, but precise erasure often comes at the cost of damaging semantically related non-target concepts. Existing value-space methods remove the component of each cross-attention value along the target concept direction, implicitly treating target identity and shared visual structure as the same signal.
arXiv Machine Learning
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Certifying Concept Unlearning in Text-to-Image Diffusion Models
The paper introduces a certification framework for assessing concept unlearning in text-to-image diffusion models, offering high‑confidence guarantees with bounded error on residual concept leakage. Unlike prior methods that rely solely on attack success rates from automated prompt searches, this approach combines statistical certification with worst‑case analysis along concept‑relevant embedding directions to derive explicit upper bounds on leakage probability. Evaluations across NSFW content, artistic styles, and celebrity identities reveal that certified leakage bounds exceed standard attack success rates by 16.2%, highlighting significant residual risks overlooked by existing protocols.