Enhancing the Transferability of Adversarial Point Clouds by Initializing Transferable Adversarial Noise

Abstract

One of the most popular methods for analyzing the robustness of 3D Deep Neural Networks (DNNs) is the transfer-based adversarial attack method, as it allows to analyze the robustness of an unknown model by generating an adversarial point cloud on an alternative model. However, the adversarial point clouds generated by current methods may overfit the surrogate models that generated them, thus limiting their performance in transfer attacks against different target 3D classifiers. To enhance the transferability of the adversarial point cloud, we propose in this letter an adversarial attack method by Initializing the Transferable Adversarial Noise, which named as ITAN . Specifically, we pre-train on the training set a generator capable of generating the adversarial noise with transferability and diversity, and then the noise generated by the generator serves as the initial adversarial noise to be integrated into the iterations of the attack. Extensive experiments on well-recognized benchmark datasets demonstrate that the adversarial point clouds generated by the proposed ITAN could be effectively transferred across unknown 3D classifiers.