Detection of Adversarial Attacks via Disentangling Natural Images and Perturbations-Reference-Cited by-同舟云学术

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Detection of Adversarial Attacks via Disentangling Natural Images and Perturbations

Published:2024 Issue: Volume:19 Page:2814-2825
ISSN:1556-6013
Container-title:IEEE Transactions on Information Forensics and Security
language:
Short-container-title:IEEE Trans.Inform.Forensic Secur.

Author:

Qing Yuanyuan¹^ORCID,Bai Tao¹^ORCID,Liu Zhuotao²,Moulin Pierre³,Wen Bihan¹^ORCID

Affiliation:

1. School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, Singapore

2. Institute for Network Sciences and Cyberspace, Tsinghua University, Beijing, China

3. Department of Electrical and Computer Engineering, University of Illinois at Urbana—Champaign, Champaign, IL, USA

Funder

Singapore Ministry of Education AcRF Tier 1

Start-Up Grant

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/10206/10319981/10404044.pdf?arnumber=10404044

Reference59 articles.

1. Intriguing properties of neural networks;Szegedy;arXiv:1312.6199,2013

2. Explaining and harnessing adversarial examples;Goodfellow;arXiv:1412.6572,2014

3. Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving

4. PhysGAN: Generating Physical-World-Resilient Adversarial Examples for Autonomous Driving

5. Deep learning models for electrocardiograms are susceptible to adversarial attack

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Generating Adversarial Texts by the Universal Tail Word Addition Attack;Lecture Notes in Computer Science;2024

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