Summary of Towards Million-scale Adversarial Robustness Evaluation with Stronger Individual Attacks, by Yong Xie and Weijie Zheng and Hanxun Huang and Guangnan Ye and Xingjun Ma
Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks
by Yong Xie, Weijie Zheng, Hanxun Huang, Guangnan Ye, Xingjun Ma
First submitted to arxiv on: 20 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)
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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 The paper focuses on evaluating the vulnerabilities of deep learning models to adversarial perturbations, essential for ensuring their reliability in safety-critical applications. Despite advances in white-box adversarial robustness evaluation methods, challenges persist in conducting comprehensive tests, particularly at large scales. This work proposes a novel individual attack method, Probability Margin Attack (PMA), which outperforms current state-of-the-art methods. Building on PMA, the authors propose two ensemble attacks that balance effectiveness and efficiency. A million-scale dataset, CC1M, is created to conduct the first million-scale white-box adversarial robustness evaluation of ImageNet models, revealing valuable insights into robustness gaps between individual and ensemble attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure deep learning models are safe from bad data that could make them behave strangely. These models are used in important things like self-driving cars, so we need to test them carefully. The researchers came up with a new way to test these models, called Probability Margin Attack (PMA), which is better than other methods. They also created a huge dataset with one million examples to test the models on. This helps us understand how well the models can handle bad data and what we need to do to make them safer. |
Keywords
* Artificial intelligence * Deep learning * Probability
