Summary of Searching Realistic-looking Adversarial Objects For Autonomous Driving Systems, by Shengxiang Sun et al.
Searching Realistic-Looking Adversarial Objects For Autonomous Driving Systems
by Shengxiang Sun, Shenzhe Zhu
First submitted to arxiv on: 19 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 proposes a modified gradient-based texture optimization method to discover realistic-looking adversarial objects for self-driving policies. The approach builds upon prior research and incorporates an entity called the “Judge” that assesses the texture of rendered objects, assigning a probability score reflecting their realism. The Judge’s score is integrated into the loss function to encourage the NeRF object renderer to learn both realistic and adversarial textures simultaneously. The paper evaluates four strategies for developing a robust Judge: leveraging cutting-edge vision-language models, fine-tuning open-sourced vision-language models, pretraining neurosymbolic systems, or utilizing traditional image processing techniques. The findings suggest that strategies 1) and 4) are less reliable, while strategies 2) and 3) may be more promising directions for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make self-driving cars safer by finding realistic-looking fake objects that can trick the car’s AI into making bad decisions. The researchers use a new method to create these fake objects, which they call “adversarial objects.” This method involves an agent called the “Judge” that looks at the fake object and says whether it looks real or not. The Judge helps the computer learn to make both realistic and fake objects at the same time. The researchers tested different ways to train the Judge and found that some methods work better than others. |
Keywords
» Artificial intelligence » Fine tuning » Loss function » Optimization » Pretraining » Probability
