Summary of On the Robustness Of Adversarial Training Against Uncertainty Attacks, by Emanuele Ledda et al.
On the Robustness of Adversarial Training Against Uncertainty Attacks
by Emanuele Ledda, Giovanni Scodeller, Daniele Angioni, Giorgio Piras, Antonio Emanuele Cinà, Giorgio Fumera, Battista Biggio, Fabio Roli
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 presents a study on uncertainty estimation in learning problems, with a focus on security-sensitive applications. It discusses how noise inherent to tasks can hinder inference and highlights the importance of trustworthy uncertainty measures. The authors demonstrate that defending against adversarial examples, which are carefully perturbed samples designed to cause misclassification, also guarantees more secure and trustworthy uncertainty estimates without requiring an ad-hoc defense strategy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers investigate how to get reliable uncertainty estimates in learning problems. They show that making models resistant to special types of mistakes (called adversarial examples) can also make those models better at providing accurate uncertainty measures. The authors tested their ideas on two popular datasets and showed that it works. |
Keywords
» Artificial intelligence » Inference
