  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.
