Concept Erasure

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Overview

Concept erasure studies how to suppress targeted information—such as unsafe content, personal identities, or artistic styles—without damaging a model’s general capabilities. In generative models, common approaches fine-tune weights or use inference-time steering, while representation-based methods identify and modify concept directions, subspaces, or attention pathways. Researchers test these methods on image and video generation, language models, and other settings, measuring both target leakage and preservation of unrelated outputs, including under adversarial prompts. A central challenge is that erased concepts can remain recoverable through alternate representations or prompts, while stronger suppression can cause collateral changes, so reliable evaluation and balancing removal against utility remain open problems.

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61 papers7 papers in the last four weeks, up 17% on the four weeks before. 0.0% of all new papers.

Feb 18, 2025

Comprehensive Assessment and Analysis for NSFW Content Erasure in Text-to-Image Diffusion Models

Text-to-image diffusion models have gained widespread application across various domains, demonstrating remarkable creative potential. However, the strong generalization capabilities of diffusion models can inadvertently lead to the generation of not-safe-for-work (NSFW) content, posing significant risks to their safe deployment. While several concept erasure methods have been proposed to mitigate the issue associated with NSFW content, a comprehensive evaluation of their effectiveness across various scenarios remains absent. To bridge this gap, we introduce a full-pipeline toolkit specifically designed for concept erasure and conduct the first systematic study of NSFW concept erasure methods. By examining the interplay between the underlying mechanisms and empirical observations, we provide in-depth insights and practical guidance for the effective application of concept erasure methods in various real-world scenarios, with the aim of advancing the understanding of content safety in diffusion models and establishing a solid foundation for future research and development in this critical area.

Die Chen, Zhiwen Li, Cen Chen +5

East China Normal University

T2I GenerationConcept ErasureT2I Generation EvaluationT2I Diffusion Models
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