Symmetry Defense Against XGBoost Adversarial Perturbation Attacks
Blerta Lindqvist

TL;DR
This paper explores how symmetry-based defenses can significantly improve the robustness of gradient-boosting decision trees (GBDTs) against adversarial attacks, achieving near-perfect accuracy in challenging scenarios.
Contribution
It demonstrates that GBDTs lack invariance to symmetries and introduces a symmetry defense method that substantially enhances adversarial robustness.
Findings
Up to 100% accuracy against zero-knowledge attacks.
Over 95% accuracy against perfect-knowledge attacks on F-MNIST.
First application of symmetry defense to GBDTs.
Abstract
We examine whether symmetry can be used to defend tree-based ensemble classifiers such as gradient-boosting decision trees (GBDTs) against adversarial perturbation attacks. The idea is based on a recent symmetry defense for convolutional neural network classifiers (CNNs) that utilizes CNNs' lack of invariance with respect to symmetries. CNNs lack invariance because they can classify a symmetric sample, such as a horizontally flipped image, differently from the original sample. CNNs' lack of invariance also means that CNNs can classify symmetric adversarial samples differently from the incorrect classification of adversarial samples. Using CNNs' lack of invariance, the recent CNN symmetry defense has shown that the classification of symmetric adversarial samples reverts to the correct sample classification. In order to apply the same symmetry defense to GBDTs, we examine GBDT invariance…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning
MethodsFLIP
