A Hybrid Training-Time and Run-Time Defense Against Adversarial Attacks in Modulation Classification

Author:

Zhang Lu1ORCID,Lambotharan Sangarapillai2ORCID,Zheng Gan2ORCID,Liao Guisheng1ORCID,Demontis Ambra3ORCID,Roli Fabio4

Affiliation:

1. School of Electronic Engineering, Xidian University, Xi’an, China

2. Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough, U.K.

3. Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy

4. Department of Informatics, Bioengineering, Robotics, and Systems Engineering, University of Genova, Genoa, Italy

Funder

Engineering and Physical Sciences Research Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering

Reference15 articles.

1. Countermeasures Against Adversarial Examples in Radio Signal Classification

2. CAT: Customized adversarial training for improved robustness;cheng;arXiv 2002 06789,2020

3. Towards deep learning models resistant to adversarial attacks;madry;Proc Int Conf Learn Represent,2018

4. MMA training: Direct input space margin maximization through adversarial training;ding;Proc Int Conf Learn Represent,2019

5. Is Deep Learning Safe for Robot Vision? Adversarial Examples Against the iCub Humanoid

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