Summary of Ungeneralizable Examples, by Jingwen Ye et al.
Ungeneralizable Examples
by Jingwen Ye, Xinchao Wang
First submitted to arxiv on: 22 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposes a new approach to creating unlearnable data for deep learning models, called UGEntities (UGEs), which balances authorized learnability with unauthorized unlearnability. The authors introduce the concept of conditional data learnability and demonstrate that UGEs can be trained to match the gradients of original data while maintaining unlearnability for potential hackers. To ensure data usability and prevent unauthorized learning, the authors optimize UGEs by maximizing a designated distance loss in a common feature space. Additionally, they propose undistillation optimization to safeguard against potential attacks. Experimental results on multiple datasets and networks show that the proposed framework preserves data usability while reducing training performance on hacker networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper finds a way to make data more secure for deep learning models. Currently, we rely on publicly available data, which is a risk because anyone can access it. To fix this, researchers created small noises in the data, but these noises limited how useful the data was. In this new approach, they create “ungeneralizable examples” that are only learnable by authorized users, making it harder for hackers to get information. They also add extra protection against attacks. The results show that this approach makes data usable while keeping it secure. |
Keywords
» Artificial intelligence » Deep learning » Optimization
