Summary of Chain-of-jailbreak Attack For Image Generation Models Via Editing Step by Step, By Wenxuan Wang et al.
Chain-of-Jailbreak Attack for Image Generation Models via Editing Step by Step
by Wenxuan Wang, Kuiyi Gao, Zihan Jia, Youliang Yuan, Jen-tse Huang, Qiuzhi Liu, Shuai Wang, Wenxiang Jiao, Zhaopeng Tu
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
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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 The paper introduces a novel jailbreaking method called Chain-of-Jailbreak (CoJ) attack, which compromises image generation models by decomposing malicious queries into multiple sub-queries. The CoJ-Bench dataset is constructed to evaluate the effectiveness of this method, comprising nine safety scenarios, three editing operations, and three editing elements. Experiments on four widely-used image generation services show that the CoJ attack can successfully bypass safeguards in over 60% of cases, outperforming other jailbreaking methods. Additionally, the paper proposes a prompting-based method called Think Twice Prompting to enhance model safety against the CoJ attack, with a success rate of over 95%. This research has implications for content creation and publishing workflows. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding ways to trick image generation models into making harmful or inappropriate images. The authors created a special way to do this called the Chain-of-Jailbreak (CoJ) attack, which works by breaking down bad requests into smaller parts that the model can’t stop. They tested their method on four different image-generating services and found that it worked over 60% of the time! To make these models safer, they also came up with a way to ask the right questions so that the model won’t produce harmful images. |
Keywords
» Artificial intelligence » Image generation » Prompting
