  With recent developments of convolutional neural networks, deep learning for
3D point clouds has shown significant progress in various 3D scene
understanding tasks, e.g., object recognition, semantic segmentation. In a
safety-critical environment, it is however not well understood how such deep
learning models are vulnerable to adversarial examples. In this work, we
explore adversarial attacks for point cloud-based neural networks. We propose a
unified formulation for adversarial point cloud generation that can generalise
two different attack strategies. Our method generates adversarial examples by
attacking the classification ability of point cloud-based networks while
considering the perceptibility of the examples and ensuring the minimal level
of point manipulations. Experimental results show that our method achieves the
state-of-the-art performance with higher than 89% and 90% of attack success
rate on synthetic and real-world data respectively, while manipulating only
about 4% of the total points.
