  3D object classification and segmentation using deep neural networks has been
extremely successful. As the problem of identifying 3D objects has many
safety-critical applications, the neural networks have to be robust against
adversarial changes to the input data set. There is a growing body of research
on generating human-imperceptible adversarial attacks and defenses against them
in the 2D image classification domain. However, 3D objects have various
differences with 2D images, and this specific domain has not been rigorously
studied so far.
  We present a preliminary evaluation of adversarial attacks on deep 3D point
cloud classifiers, namely PointNet and PointNet++, by evaluating both white-box
and black-box adversarial attacks that were proposed for 2D images and
extending those attacks to reduce the perceptibility of the perturbations in 3D
space. We also show the high effectiveness of simple defenses against those
attacks by proposing new defenses that exploit the unique structure of 3D point
clouds. Finally, we attempt to explain the effectiveness of the defenses
through the intrinsic structures of both the point clouds and the neural
network architectures. Overall, we find that networks that process 3D point
cloud data are weak to adversarial attacks, but they are also more easily
defensible compared to 2D image classifiers. Our investigation will provide the
groundwork for future studies on improving the robustness of deep neural
networks that handle 3D data.
