Summary of Attack End-to-end Autonomous Driving Through Module-wise Noise, by Lu Wang et al.
Attack End-to-End Autonomous Driving through Module-Wise Noise
by Lu Wang, Tianyuan Zhang, Yikai Han, Muyang Fang, Ting Jin, Jiaqi Kang
First submitted to arxiv on: 12 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 investigates the security risks associated with deep learning models used in autonomous driving, particularly end-to-end architectures. Despite their success in various tasks, these models are vulnerable to adversarial attacks, which can compromise the safety and reliability of autonomous vehicles. The authors propose a novel attack method, module-wise noise injection, that exploits vulnerabilities in the model inference process. They demonstrate the effectiveness of this approach through large-scale experiments on a full-stack autonomous driving model, outperforming previous attack methods. This research aims to provide insights into ensuring the security of autonomous driving systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how deep learning models used in self-driving cars can be tricked or attacked. Currently, these models are very good at doing lots of tasks like recognizing objects and making decisions. But if someone tries to make them fail, they can be really bad at those things! The people who wrote this paper found a new way to do that, by adding special noise to the model’s thinking process. They tested it on a big self-driving car model and showed that their method was better than other ways of doing it. This research wants to help make sure self-driving cars are safe. |
Keywords
» Artificial intelligence » Deep learning » Inference
