Minimal Adversarial Examples for Deep Learning on 3D Point Clouds

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, object detection. 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 general formulation for adversarial point cloud generation via $\ell_0$-norm optimisation. Our method generates adversarial examples by attacking the classification ability of the point cloud-based networks while considering the perceptibility of the examples and ensuring the minimum level of point manipulations. The proposed method is general and can be realised in different attack strategies. Experimental results show that our method achieves the state-of-the-art performance with higher than 89% and 90% of attack success on synthetic and real-world data respectively, while manipulating only about 4% of the total points.