Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective-Reference-Cited by-同舟云学术

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Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective

Published:2022 Issue: Volume: Page:1-1
ISSN:1545-5971
Container-title:IEEE Transactions on Dependable and Secure Computing
language:
Short-container-title:IEEE Trans. Dependable and Secure Comput.

Author:

Meng Mark Huasong¹,Bai Guangdong²,Teo Sin Gee³,Hou Zhe⁴,Xiao Yan⁵,Lin Yun⁵,Dong Jin Song⁵

Affiliation:

1. National University of Singapore, and also with the Institute for Infocomm Research, A*STAR, Singapore

2. School of Information Technology and Electrical Engineering, The University of QueenslandAustralia

3. Institute for Infocomm Research, A*STAR, Singapore

4. Griffith University, Australia

5. National University of Singapore

Funder

Ministry of Education - Singapore

National Satellite of Excellence in Trustworthy Software Systems NSOE-TSS Programme

Institute for Infocomm Research

University of Queensland

Cisco-NUS Accelerated Digital Economy Corporate Laboratory

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/8858/4358699/09785704.pdf?arnumber=9785704

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