Title: Geometry-aware Generation of Adversarial and Cooperative Point Clouds. Abstract: Recent studies show that machine learning models are vulnerable to adversarial examples. In 2D image domain, these examples are obtained by adding imperceptible noises to natural images. This paper studies adversarial generation of point clouds by learning to deform those approximating object surfaces of certain categories. As 2D manifolds embedded in the 3D Euclidean space, object surfaces enjoy the general properties of smoothness and fairness. We thus argue that in order to achieve imperceptible surface shape deformations, adversarial point clouds should have the same properties with similar degrees of smoothness/fairness to the benign ones, while being close to the benign ones as well when measured under certain distance metrics of point clouds. To this end, we propose a novel loss function to account for imperceptible, geometry-aware deformations of point clouds, and use the proposed loss in an adversarial objective to attack representative models of point set classifiers. Experiments show that our proposed method achieves stronger attacks than existing methods, without introduction of noticeable outliers and surface irregularities. In this work, we also investigate an opposite direction that learns to deform point clouds of object surfaces in the same geometry-aware, but cooperative manner. Cooperatively generated point clouds are more favored by machine learning models in terms of improved classification confidence or accuracy. We present experiments verifying that our proposed objective succeeds in learning cooperative shape deformations.