3D Point Cloud Adversarial Attacks and Defenses
Adversarial attacks and defenses on neural networks that process 3D point cloud data, namely PointNet and PointNet++. The preprint paper is available on Arxiv here.
Note that files modified from the PointNet and PointNet++ source codes are included. Some files may need to be moved to the correct location before running experiments.
- Fast/iterative gradient sign
- Jacobian-based saliency map attack
- Gradient projection
- Clipping L2 norms
- Adversarial training
- Outlier removal
- Salient point removal
- Adversarial attacks are effective against deep 3D point cloud classifiers
- It is more easy to defend point cloud classifiers than 2D image classifiers