  Utilizing 3D point cloud data has become an urgent need for the deployment of
artificial intelligence in many areas like facial recognition and self-driving.
However, deep learning for 3D point clouds is still vulnerable to adversarial
attacks, e.g., iterative attacks, point transformation attacks, and generative
attacks. These attacks need to restrict perturbations of adversarial examples
within a strict bound, leading to the unrealistic adversarial 3D point clouds.
In this paper, we propose an Adversarial Graph-Convolutional Generative
Adversarial Network (AdvGCGAN) to generate visually realistic adversarial 3D
point clouds from scratch. Specifically, we use a graph convolutional generator
and a discriminator with an auxiliary classifier to generate realistic point
clouds, which learn the latent distribution from the real 3D data. The
unrestricted adversarial attack loss is incorporated in the special adversarial
training of GAN, which enables the generator to generate the adversarial
examples to spoof the target network. Compared with the existing state-of-art
attack methods, the experiment results demonstrate the effectiveness of our
unrestricted adversarial attack methods with a higher attack success rate and
visual quality. Additionally, the proposed AdvGCGAN can achieve better
performance against defense models and better transferability than existing
attack methods with strong camouflage.
