Summary of Safety Alignment Backfires: Preventing the Re-emergence Of Suppressed Concepts in Fine-tuned Text-to-image Diffusion Models, by Sanghyun Kim et al.
Safety Alignment Backfires: Preventing the Re-emergence of Suppressed Concepts in Fine-tuned Text-to-Image Diffusion Models
by Sanghyun Kim, Moonseok Choi, Jinwoo Shin, Juho Lee
First submitted to arxiv on: 30 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates a critical vulnerability in fine-tuning text-to-image diffusion models. Specifically, it highlights that safety alignment methods designed to filter out harmful content can break down during fine-tuning, allowing previously suppressed content to resurface even when using benign datasets. This phenomenon is known as “fine-tuning jailbreaking.” The authors reveal that standard fine-tuning can inadvertently undo safety measures, causing models to relearn harmful concepts and exacerbate harmful behaviors. To address this issue, the paper presents a novel solution called Modular LoRA, which involves training Safety Low-Rank Adaptation modules separately from Fine-Tuning LoRA components and merging them during inference. The authors demonstrate that Modular LoRA outperforms traditional fine-tuning methods in maintaining safety alignment, offering a practical approach for enhancing the security of text-to-image diffusion models against potential attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a problem with using machine learning models to generate images. It seems that when we make these models more specific to certain tasks or topics, they can accidentally learn bad things again that we previously removed. This is called “fine-tuning jailbreaking.” The authors are trying to find a way to fix this issue so that our image generation models don’t start producing harmful content again. They propose a new method called Modular LoRA that helps keep the models safe and secure. |
Keywords
» Artificial intelligence » Alignment » Fine tuning » Image generation » Inference » Lora » Low rank adaptation » Machine learning
