Summary of Evaluating the Adversarial Robustness Of Semantic Segmentation: Trying Harder Pays Off, by Levente Halmosi et al.
Evaluating the Adversarial Robustness of Semantic Segmentation: Trying Harder Pays Off
by Levente Halmosi, Bálint Mohos, Márk Jelasity
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 focuses on evaluating the vulnerability of machine learning models to tiny adversarial input perturbations in semantic segmentation tasks. The authors argue that current evaluation methodologies are insufficient and propose new attacks combined with existing ones to measure the sensitivity of robust segmentation models. They re-evaluate well-known models, analyzing their performance under various attacks, and find that most state-of-the-art models have a larger sensitivity to adversarial perturbations than previously reported. Additionally, they demonstrate a size-bias phenomenon where small objects are more easily attacked despite large objects being robust. The authors conclude that a diverse set of strong attacks is necessary due to the varying vulnerabilities of different models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper looks at how well machine learning models can handle tiny changes in their input data without breaking down. Right now, we have ways to test this for image classification, but it’s not as good for semantic segmentation. The authors tested some popular models and found that they’re more vulnerable than we thought. They also discovered that small objects are easier to mess up even if big ones are fine. This means we need better tests to figure out how well our models can handle these tiny changes. |
Keywords
» Artificial intelligence » Image classification » Machine learning » Semantic segmentation
