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arXiv · 1906.03310

Robustness for Non-Parametric Classification: A Generic Attack and Defense

Yao-Yuan Yang·Cyrus Rashtchian·Yizhen Wang·Kamalika Chaudhuri
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Abstract

Adversarially robust machine learning has received much recent attention. However, prior attacks and defenses for non-parametric classifiers have been developed in an ad-hoc or classifier-specific basis. In this work, we take a holistic look at adversarial examples for non-parametric classifiers, including nearest neighbors, decision trees, and random forests. We provide a general defense method, adversarial pruning, that works by preprocessing the dataset to become well-separated. To test our defense, we provide a novel attack that applies to a wide range of non-parametric classifiers. Theoretically, we derive an optimally robust classifier, which is analogous to the Bayes Optimal. We show that adversarial pruning can be viewed as a finite sample approximation to this optimal classifier. We empirically show that our defense and attack are either better than or competitive with prior work on non-parametric classifiers. Overall, our results provide a strong and broadly-applicable baseline for future work on robust non-parametrics. Code available at https://github.com/yangarbiter/adversarial-nonparametrics/ .

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Yao-Yuan Yang, Cyrus Rashtchian, Yizhen Wang, Kamalika Chaudhuri. 2020-02-24. Robustness for Non-Parametric Classification: A Generic Attack and Defense. https://arxiv.org/abs/1906.03310

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