Robust Decision Trees Against Adversarial Examples

TL;DR

This paper introduces a novel method to enhance the robustness of tree-based models against adversarial examples by optimizing for worst-case input perturbations, making them more resilient in practical scenarios.

Contribution

The paper develops a scalable algorithm for training robust decision trees by approximating the saddle point problem, applicable to classical and boosting tree models.

Findings

Robust trees significantly outperform standard trees under adversarial attacks.

The proposed method is effective on real-world datasets.

Scalable algorithms enable practical robust tree training.

Abstract

Although adversarial examples and model robustness have been extensively studied in the context of linear models and neural networks, research on this issue in tree-based models and how to make tree-based models robust against adversarial examples is still limited. In this paper, we show that tree based models are also vulnerable to adversarial examples and develop a novel algorithm to learn robust trees. At its core, our method aims to optimize the performance under the worst-case perturbation of input features, which leads to a max-min saddle point problem. Incorporating this saddle point objective into the decision tree building procedure is non-trivial due to the discrete nature of trees --- a naive approach to finding the best split according to this saddle point objective will take exponential time. To make our approach practical and scalable, we propose efficient tree building algorithms by approximating the inner minimizer in this saddle point problem, and present efficient implementations for classical information gain based trees as well as state-of-the-art tree boosting models such as XGBoost. Experimental results on real world datasets demonstrate that the proposed algorithms can substantially improve the robustness of tree-based models against adversarial examples.