Fantastic Targets for Concept Erasure in Diffusion Models and Where To Find Them

• URL: http://arxiv.org/abs/2501.18950v2
• Date: Thu, 27 Feb 2025 23:36:38 GMT
• Title: Fantastic Targets for Concept Erasure in Diffusion Models and Where To Find Them
• Authors: Anh Bui, Trang Vu, Long Vuong, Trung Le, Paul Montague, Tamas Abraham, Junae Kim, Dinh Phung,
• Abstract summary: Concept erasure has emerged as a promising technique for mitigating the risk of harmful content generation in diffusion models.<n>We propose the Adaptive Guided Erasure (AGE) method, which emphdynamically selects optimal target concepts tailored to each undesirable concept.<n>Results show that AGE significantly outperforms state-of-the-art erasure methods on preserving unrelated concepts while maintaining effective erasure performance.
• Score: 21.386640828092524
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Concept erasure has emerged as a promising technique for mitigating the risk of harmful content generation in diffusion models by selectively unlearning undesirable concepts. The common principle of previous works to remove a specific concept is to map it to a fixed generic concept, such as a neutral concept or just an empty text prompt. In this paper, we demonstrate that this fixed-target strategy is suboptimal, as it fails to account for the impact of erasing one concept on the others. To address this limitation, we model the concept space as a graph and empirically analyze the effects of erasing one concept on the remaining concepts. Our analysis uncovers intriguing geometric properties of the concept space, where the influence of erasing a concept is confined to a local region. Building on this insight, we propose the Adaptive Guided Erasure (AGE) method, which \emph{dynamically} selects optimal target concepts tailored to each undesirable concept, minimizing unintended side effects. Experimental results show that AGE significantly outperforms state-of-the-art erasure methods on preserving unrelated concepts while maintaining effective erasure performance. Our code is published at {https://github.com/tuananhbui89/Adaptive-Guided-Erasure}.

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