Minimal Adversarial Examples for Deep Learning on 3D Point Clouds

Minimal Adversarial Examples for Deep Learning on 3D Point Clouds

Authors: Jaeyeon Kim, Binh-Son Hua, Duc Thanh Nguyen, Sai-Kit Yeung

ICCV 2021

AbstractPDFBibtex

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, semantic segmentation. In a safetycritical 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 generation that can generalise two different attack strategies. Our method generates adversarial examples by attacking the classification ability of point cloud-based networks while considering the perceptibility of the examples and ensuring the minimal level of point manipulations. Experimental results show that our method achieves the state-of-the-art performance with higher than 89% and 90% of attack success rate on synthetic and real-world data respectively, while manipulating only about 4% of the total points.

@InProceedings{Kim_2021_ICCV,
author = {Kim, Jaeyeon and Hua, Binh-Son and Nguyen, Thanh and Yeung, Sai-Kit},
title = {Minimal Adversarial Examples for Deep Learning on 3D Point Clouds},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pages = {7797-7806}
}