# Adversarial shape perturbations on 3D point clouds

arXiv: https://arxiv.org/abs/1908.06062 · HTML: https://mlsanity.com/paper/1908.06062/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2019.

## Built on by (most cited first, 3 of 3)

- [LPF-Defense: 3D Adversarial Defense based on Frequency Analysis](https://mlsanity.com/paper/2202.11287/) (2022)
- [Adversarial Attack by Limited Point Cloud Surface Modifications](https://mlsanity.com/paper/2110.03745/) (2021)
- [Adaptive Local Adversarial Attacks on 3D Point Clouds for Augmented Reality](https://mlsanity.com/paper/2303.06641/) (2023)

## Abstract (arXiv)

The importance of training robust neural network grows as 3D data is increasingly utilized in deep learning for vision tasks in robotics, drone control, and autonomous driving. One commonly used 3D data type is 3D point clouds, which describe shape information. We examine the problem of creating robust models from the perspective of the attacker, which is necessary in understanding how 3D neural networks can be exploited. We explore two categories of attacks: distributional attacks that involve imperceptible perturbations to the distribution of points, and shape attacks that involve deforming the shape represented by a point cloud. We explore three possible shape attacks for attacking 3D point cloud classification and show that some of them are able to be effective even against preprocessing steps, like the previously proposed point-removal defenses.