Point Cloud Adversarial Perturbation Generation for Adversarial Attacks-Reference-Cited by-同舟云学术

Point Cloud Adversarial Perturbation Generation for Adversarial Attacks

Published:2023 Issue: Volume:11 Page:2767-2774
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

He Fengmei¹,Chen Yihuai²,Chen Ruidong³^ORCID,Nie Weizhi³^ORCID

Affiliation:

1. Department of Automation Electrical Engineering, Tianjing University of Technology and Education, Tianjin, China

2. College of Information Technology, Wenzhou Vocational College of Science and Technology and Education, Wenzhou Academy of Agricultural Sciences, Wenzhou, China

3. The School of Electrical and Information Engineering, Tianjin University, Tianjin, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10006822.pdf?arnumber=10006822

Reference31 articles.

1. LG-GAN: Label Guided Adversarial Network for Flexible Targeted Attack of Point Cloud Based Deep Networks

2. PointCloud Saliency Maps

3. Cross-domain transferability of adversarial perturbations;naseer;Proc Adv Neural Inf Process Syst Annu Conf Neural Inf Process Syst (NeurIPS),2019

4. DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks

5. Point cloud super resolution with adversarial residual graph networks;wu;arXiv 1908 02111,2019

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