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arXiv · 2411.12071

Theoretical Corrections and the Leveraging of Reinforcement Learning to Enhance Triangle Attack

Nicole Meng·Caleb Manicke·David Chen·Yingjie Lao·Caiwen Ding·Pengyu Hong·Kaleel Mahmood
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Abstract

Adversarial examples represent a serious issue for the application of machine learning models in many sensitive domains. For generating adversarial examples, decision based black-box attacks are one of the most practical techniques as they only require query access to the model. One of the most recently proposed state-of-the-art decision based black-box attacks is Triangle Attack (TA). In this paper, we offer a high-level description of TA and explain potential theoretical limitations. We then propose a new decision based black-box attack, Triangle Attack with Reinforcement Learning (TARL). Our new attack addresses the limits of TA by leveraging reinforcement learning. This creates an attack that can achieve similar, if not better, attack accuracy than TA with half as many queries on state-of-the-art classifiers and defenses across ImageNet and CIFAR-10.

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Nicole Meng, Caleb Manicke, David Chen, Yingjie Lao, Caiwen Ding, Pengyu Hong, Kaleel Mahmood. 2024-11-18. Theoretical Corrections and the Leveraging of Reinforcement Learning to Enhance Triangle Attack. https://arxiv.org/abs/2411.12071

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