Summary of Unseg: One Universal Unlearnable Example Generator Is Enough Against All Image Segmentation, by Ye Sun et al.
UnSeg: One Universal Unlearnable Example Generator is Enough against All Image Segmentation
by Ye Sun, Hao Zhang, Tiehua Zhang, Xingjun Ma, Yu-Gang Jiang
First submitted to arxiv on: 13 Oct 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 This paper proposes a novel framework called Unlearnable Segmentation (UnSeg) that trains a universal unlearnable noise generator to transform downstream images into their unusable versions for large-scale image segmentation model training. The noise generator is fine-tuned from the Segment Anything Model (SAM) via bilevel optimization on an interactive segmentation dataset, aiming to minimize the training error of a surrogate model with the same architecture as SAM but trained from scratch. The effectiveness of UnSeg is empirically verified across 6 mainstream image segmentation tasks, 10 datasets, and 7 network architectures, demonstrating that unlearnable images can significantly reduce segmentation performance. This work provides insights into leveraging foundation models in a data-efficient manner to protect images against image segmentation models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about keeping your pictures private by making it hard for computers to understand them. They’re doing this by adding special noise to the pictures that makes it difficult for machines to learn from them. This is important because some companies and organizations are training their computers using other people’s private photos without permission. The researchers created a new way to add this noise, called Unlearnable Segmentation (UnSeg), which can be used with different types of computer networks and images. They tested it on many kinds of pictures and found that it worked well in keeping the information private. |
Keywords
» Artificial intelligence » Image segmentation » Optimization » Sam
