Summary of Robustness-congruent Adversarial Training For Secure Machine Learning Model Updates, by Daniele Angioni et al.
Robustness-Congruent Adversarial Training for Secure Machine Learning Model Updates
by Daniele Angioni, Luca Demetrio, Maura Pintor, Luca Oneto, Davide Anguita, Battista Biggio, Fabio Roli
First submitted to arxiv on: 27 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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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 Machine learning models require periodic updates to improve their average accuracy, leveraging novel architectures and additional data. However, a newly updated model may commit mistakes that the previous model did not make. This phenomenon is referred to as negative flips, experienced by users as a regression of performance. The paper shows that this problem also affects robustness to adversarial examples, hindering the development of secure model update practices. In particular, updating a model to improve its adversarial robustness can cause some previously ineffective adversarial examples to become misclassified, causing a regression in perceived security. The authors propose a novel technique, named robustness-congruent adversarial training, to address this issue. This involves fine-tuning a model with adversarial training while constraining it to retain higher robustness on correctly classified adversarial examples before the update. The paper demonstrates that the algorithm and learning with non-regression constraints provide a theoretically grounded framework for training consistent estimators. Experiments on robust models for computer vision confirm that both accuracy and robustness, even if improved after model update, can be affected by negative flips, and the proposed method outperforms competing baseline methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models need to be updated regularly to get better. But sometimes, this update makes the model worse than before. This is called a “negative flip.” It’s like when you learn something new, but it actually makes things harder instead of easier. The paper shows that this problem also happens with “adversarial examples,” which are fake images designed to trick the model into making mistakes. The authors propose a new way to update models so they don’t get worse over time. They call it “robustness-congruent adversarial training.” It’s like fine-tuning a model to make sure it doesn’t forget what it learned before. |
Keywords
* Artificial intelligence * Fine tuning * Machine learning * Regression
