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标题: "PEAK: Precise and Persistent Concept Erasure via k-Sparse Autoencoders"
原文链接: "https://arxiv.org/abs/2608.10985"
发布日期: "2026-08-11"
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去重键: "https://arxiv.org/abs/2608.10985"
---

## Computer Science > Computer Vision and Pattern Recognition

## Title:PEAK: Precise and Persistent Concept Erasure via k-Sparse Autoencoders

Authors:[Man Jiang](https://arxiv.org/search/cs?searchtype=author&query=Jiang,+M), [Ouxiang Li](https://arxiv.org/search/cs?searchtype=author&query=Li,+O), [Weibao Xue](https://arxiv.org/search/cs?searchtype=author&query=Xue,+W), [Zhenhua Tang](https://arxiv.org/search/cs?searchtype=author&query=Tang,+Z), [Yuan Wang](https://arxiv.org/search/cs?searchtype=author&query=Wang,+Y), [Shuo Wang](https://arxiv.org/search/cs?searchtype=author&query=Wang,+S), [Yanbin Hao](https://arxiv.org/search/cs?searchtype=author&query=Hao,+Y)

[View PDF](https://arxiv.org/pdf/2608.10985) [HTML (experimental)](https://arxiv.org/html/2608.10985v1)

> Abstract:Erasing concepts from large-scale text-to-image (T2I) diffusion models has become increasingly crucial due to the growing concerns over copyright infringement, privacy violations, and offensive content. Existing approaches struggle to achieve both precise and persistent concept erasure: inaccurate localization of concept-related representations may cause unintended semantic interference, while incomplete removal of the underlying concept knowledge allows adversarial recovery. To address this dilemma, we propose PEAK, a \\textbf{\\textit{precise}} and \\textbf{\\textit{persistent}} concept erasure framework via k-Sparse Autoencoders (kSAEs). PEAK first trains a kSAE on internal activations of the diffusion denoising network to decompose dense representations into interpretable sparse features. By contrasting sparse activations induced by target and non-target prompts, PEAK identifies a compact set of target-specific features according to both activation strength and frequency. These localized features are then used for parameter optimization, where PEAK selectively suppresses target-related activations while preserving complementary non-target ones towards the original model. This feature-guided optimization embeds concept erasure directly into diffusion parameters, eliminating the need for additional inference-time intervention and facilitating effective persistence against adversarial attacks. Extensive experiments demonstrate that PEAK achieves effective and robust concept erasure. On the I2P benchmark, PEAK reduces NudeNet detections from 582 to 6, lowers the average attack success rate (ASR) from 96.52\\% to 5.63\\%, and preserves general generation quality on MS-COCO with a near-zero KID. Our code and models are available at: [this https URL](https://github.com/manmanTAT/PEAK)

| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| --- | --- |
| Cite as: | [arXiv:2608.10985](https://arxiv.org/abs/2608.10985) \[cs.CV\] |
|  | (or [arXiv:2608.10985v1](https://arxiv.org/abs/2608.10985v1) \[cs.CV\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.10985](https://doi.org/10.48550/arXiv.2608.10985) |

## Submission history

From: Man Jiang \[[view email](https://arxiv.org/show-email/6ef86587/2608.10985)\]  
**\[v1\]** Tue, 11 Aug 2026 14:38:01 UTC (6,993 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.10985) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
