Minimal Adversarial Examples for Deep Learning on 3D Point Clouds

@article{Kim2020MinimalAE,
  title={Minimal Adversarial Examples for Deep Learning on 3D Point Clouds},
  author={Jaeyeon Kim and Binh-Son Hua and Duc Thanh Nguyen and Sai-Kit Yeung},
  journal={2021 IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2020},
  pages={7777-7786}
}
With recent developments of convolutional neural net-works, 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… 
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Passive Defense Against 3D Adversarial Point Clouds Through the Lens of 3D Steganalysis

This work designs a 3D adversarial point cloud detector that is double-blind, that is to say, it does not rely on the exact knowledge of the adversarial attack means and victim models, and can achieve good detection performance on multiple types of 3D adversary point clouds.

Ada3Diff: Defending against 3D Adversarial Point Clouds via Adaptive Diffusion

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PointCA: Evaluating the Robustness of 3D Point Cloud Completion Models Against Adversarial Examples

PointCA is proposed, the first adversarial attack against 3D point cloud completion models that can generate adversarial point clouds that maintain high similarity with the original ones, while be- ing completed as another object with totally different semantic information.

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Model-Free Prediction of Adversarial Drop Points in 3D Point Clouds

This paper aims to provide a novel viewpoint on this problem, in which adversarial points can be predicted independently of the model, and provides further insight into DNNs for point cloud analysis, by showing which features play key roles in their decision-making process.

NormalAttack: Curvature-Aware Shape Deformation along Normals for Imperceptible Point Cloud Attack

A novel NormalAttack framework towards imperceptible adversarial attacks on point clouds is proposed, which enforces the perturbation to be concentrated along normals to deform the underlying surface of 3D point clouds, such that tiny perturbations can make the shape deformed for better attack performance.

Shape Prior Guided Attack: Sparser Perturbations on 3D Point Clouds

This paper proposes a novel method named SPGA (Shape Prior Guided Attack) to generate adversarial point cloud examples that can generate examples with a higher attack success rate, less perturbation budget and stronger transferability.

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