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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2606.01658 |
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| _version_ | 1866913178044071936 |
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| author | Zhao, Mengnan Zhang, Lihe Yin, Baocai |
| author_facet | Zhao, Mengnan Zhang, Lihe Yin, Baocai |
| contents | Text guided diffusion models have revolutionized image synthesis but also raise ethical concerns, such as privacy violation and harmful content generation. To mitigate these issues, prevailing methods typically leverage an alignment mechanism, with predefined erasure references, to fine-tune pretrained model weights. However, these techniques are intrinsically limited by the representational capacity of textual space and display high sensitivity to the choice of predefined erasure references, e.g., suboptimal references may significantly affect the model utility preservation during erasure. To overcome these limitations, we introduce CoreUnlearn, aiming to disentangle and remove the erasure-critical component of the undesirable concept. Specifically, CoreUnlearn comprises a Component Extraction Module (CEM) and a Swap Disentangling Strategy (SDS). Guided by SDS, CEM is pre-trained to decompose concept embeddings into distinct component types. Leveraging this decomposition, CoreUnlearn then removes the erasure-critical component while retaining non-critical ones by fine-tuning model weights. Extensive experiments demonstrate that CoreUnlearn achieves effective concept erasure with minimal impact on overall model performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_01658 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CoreUnlearn: Rethinking Concept Unlearning through Disentangled Component-Level Erasure in Text-guided Diffusion Models Zhao, Mengnan Zhang, Lihe Yin, Baocai Cryptography and Security Text guided diffusion models have revolutionized image synthesis but also raise ethical concerns, such as privacy violation and harmful content generation. To mitigate these issues, prevailing methods typically leverage an alignment mechanism, with predefined erasure references, to fine-tune pretrained model weights. However, these techniques are intrinsically limited by the representational capacity of textual space and display high sensitivity to the choice of predefined erasure references, e.g., suboptimal references may significantly affect the model utility preservation during erasure. To overcome these limitations, we introduce CoreUnlearn, aiming to disentangle and remove the erasure-critical component of the undesirable concept. Specifically, CoreUnlearn comprises a Component Extraction Module (CEM) and a Swap Disentangling Strategy (SDS). Guided by SDS, CEM is pre-trained to decompose concept embeddings into distinct component types. Leveraging this decomposition, CoreUnlearn then removes the erasure-critical component while retaining non-critical ones by fine-tuning model weights. Extensive experiments demonstrate that CoreUnlearn achieves effective concept erasure with minimal impact on overall model performance. |
| title | CoreUnlearn: Rethinking Concept Unlearning through Disentangled Component-Level Erasure in Text-guided Diffusion Models |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2606.01658 |