Summary of Perturbing Attention Gives You More Bang For the Buck: Subtle Imaging Perturbations That Efficiently Fool Customized Diffusion Models, by Jingyao Xu et al.
Perturbing Attention Gives You More Bang for the Buck: Subtle Imaging Perturbations That Efficiently Fool Customized Diffusion Models
by Jingyao Xu, Yuetong Lu, Yandong Li, Siyang Lu, Dongdong Wang, Xiang Wei
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
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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 Medium Difficulty summary: Diffusion models have revolutionized generative modeling, enabling the efficient creation of high-quality data samples. However, their widespread adoption has raised concerns about model security, prompting the development of more effective adversarial attackers on DMs to understand its vulnerabilities. This paper proposes CAAT, a simple and efficient approach that leverages subtle perturbations on published images to corrupt generated images. By exploiting cross-attention layers’ sensitivity to gradient changes, CAAT can effectively fool latent diffusion models (LDMs) without requiring costly training. The proposed method outperforms baseline attack methods in terms of effectiveness and efficiency, demonstrating its compatibility with diverse diffusion models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper is about making sure that computers don’t get fooled into thinking fake images are real. It’s a new way to test how well these computers can create realistic pictures. The idea is to take a real picture and make it slightly different, just enough to trick the computer. This makes the computer generate a bad picture instead of a good one. The researchers found that this method works really well and is faster than other methods they tried. |
Keywords
» Artificial intelligence » Cross attention » Diffusion » Prompting
