Classification of 3D point clouds is a challenging machine learning (ML) task
with important real-world applications in a spectrum from autonomous driving
and robot-assisted surgery to earth observation from low orbit. As with other
ML tasks, classification models are notoriously brittle in the presence of
adversarial attacks. These are rooted in imperceptible changes to inputs with
the effect that a seemingly well-trained model ends up misclassifying the
input. This paper adds to the understanding of adversarial attacks by
presenting Eidos, a framework providing Efficient Imperceptible aDversarial
attacks on 3D pOint cloudS. Eidos supports a diverse set of imperceptibility
metrics. It employs an iterative, two-step procedure to identify optimal
adversarial examples, thereby enabling a runtime-imperceptibility trade-off. We
provide empirical evidence relative to several popular 3D point cloud
classification models and several established 3D attack methods, showing Eidos'
superiority with respect to efficiency as well as imperceptibility.

该研究论文阐述了针对 3D 点云的机器学习分类任务中存在的对抗攻击问题，并提出了 Eidos 框架，该框架支持高效的难以察觉的对抗攻击方法，通过迭代的两步骤流程，能够找到最佳的对抗性示例，从而平衡运行效率和对抗攻击的察觉程度。研究实验证明，相对于其他方法，Eidos 在效率和隐蔽性方面表现优越。