Summary of On the Multi-modal Vulnerability Of Diffusion Models, by Dingcheng Yang et al.
On the Multi-modal Vulnerability of Diffusion Models
by Dingcheng Yang, Yang Bai, Xiaojun Jia, Yang Liu, Xiaochun Cao, Wenjian Yu
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); 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 The paper explores the vulnerabilities of diffusion models when handling multiple modalities, specifically text and image features. By visualizing both feature spaces embedded by diffusion models, researchers find that prompts are chaotically distributed in the text space but clustered by subject in the image space, suggesting a potential misalignment in robustness between modalities. To address this, the authors propose MMP-Attack, a novel manipulation technique that leverages multi-modal priors to manipulate generation results and induce diffusion models to generate specific objects while eliminating original ones. The approach demonstrates superior manipulation capability and efficiency compared to existing studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, researchers are trying to understand how well computers can make images by using words. They found that when they tried to mix text and image features together, the computer’s performance got worse. To fix this, they came up with a new way to trick the computer into making specific images while removing others. This method works better than previous attempts. |
Keywords
* Artificial intelligence * Diffusion * Multi modal
