Summary of A Practical Approach to Evaluating the Adversarial Distance For Machine Learning Classifiers, by Georg Siedel et al.
A practical approach to evaluating the adversarial distance for machine learning classifiers
by Georg Siedel, Ekagra Gupta, Andrey Morozov
First submitted to arxiv on: 5 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 proposed research investigates methods for accurately computing adversarial robustness in machine learning (ML) classifiers, which is crucial for ensuring consistent performance in real-world applications where models may encounter corrupted or adversarial inputs. The authors aim to provide a comprehensive evaluation of adversarial robustness by estimating the upper and lower bounds of the adversarial distance using iterative adversarial attacks and a certification approach. They demonstrate the effectiveness of their adversarial attack approach compared to related implementations, while noting that the certification method falls short of expectations. This research has implications for protecting systems from vulnerabilities and ensuring safety in use. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning classifiers need to be robust against corrupted or malicious inputs to perform well in real-world applications. The problem is to estimate how far a classifier can withstand these attacks without failing. Researchers have been working on solving this problem, but current methods are limited. This paper proposes new methods that provide better estimates of the distance from normal inputs to adversarial ones. They test their approach and find it works well, which could help protect systems from vulnerabilities. |
Keywords
» Artificial intelligence » Machine learning
