Summary of A Grey-box Attack Against Latent Diffusion Model-based Image Editing by Posterior Collapse, By Zhongliang Guo et al.
A Grey-box Attack against Latent Diffusion Model-based Image Editing by Posterior Collapse
by Zhongliang Guo, Chun Tong Lei, Lei Fang, Shuai Zhao, Yifei Qian, Jingyu Lin, Zeyu Wang, Cunjian Chen, Ognjen Arandjelović, Chun Pong Lau
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 A recent advancement in Latent Diffusion Models (LDMs) has enabled unprecedented image synthesis and manipulation capabilities. However, this raises concerns about data misappropriation and intellectual property infringement. Existing methods to safeguard images from LDM manipulation are limited by reliance on model-specific knowledge and inability to significantly degrade semantic quality of generated images. The proposed Posterior Collapse Attack (PCA) minimizes dependence on target models’ white-box information, instead using a small amount of LDM parameters to cause significant semantic collapse in generation quality, particularly perceptual consistency. Experimental results show that PCA outperforms existing techniques, offering a more robust and generalizable solution for alleviating socio-technical challenges posed by generative AI. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a special kind of computer program that can create new images from scratch or change old ones to make them look different. This is called “generative AI.” But some people are worried about how this technology might be used to copy someone else’s work without permission. To help solve this problem, researchers came up with a new idea called the Posterior Collapse Attack (PCA). It works by using only a little bit of information from these image-generating programs and then making it create really bad or weird images instead of good ones. The team tested their method and found that it worked better than other methods for stopping this kind of misuse. |
Keywords
» Artificial intelligence » Diffusion » Image synthesis » Pca
