Summary of Mip: Clip-based Image Reconstruction From Peft Gradients, by Peiheng Zhou et al.
MIP: CLIP-based Image Reconstruction from PEFT Gradients
by Peiheng Zhou, Ming Hu, Xiaofei Xie, Yihao Huang, Kangjie Chen, Mingsong Chen
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 Contrastive Language-Image Pre-training (CLIP) is a pre-trained multimodal neural network widely used in distributed machine learning tasks, particularly Federated Learning (FL). This paper analyzes the vulnerability of CLIP-based FL to image reconstruction attacks. The authors propose Multm-In-Parvo (MIP), a proprietary attack method that reconstructs CLIP training images using gradients of soft prompts or adapters. MIP includes label prediction and inverse gradient estimation mechanisms to improve convergence and avoid vanishing gradients. Experimental results demonstrate the effectiveness of MIP in reconstructing training images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a powerful tool that helps computers learn together without sharing all their information. This tool is called Contrastive Language-Image Pre-training (CLIP). It’s very good at helping computers work together on tasks like image recognition and text understanding. But, what if someone wanted to take advantage of this power? In this paper, the authors show how they can use gradients from CLIP to recreate the original images used for training. They propose a method called Multm-In-Parvo (MIP) that can do this quickly and efficiently. The results are impressive and demonstrate the potential risks of using powerful AI tools like CLIP. |
Keywords
* Artificial intelligence * Federated learning * Machine learning * Neural network
