Toward Robust Networks against Adversarial Attacks for Radio Signal Modulation Classification
Author:
Affiliation:
1. Indian Institute of Technology Guwahati,Department of Electronics & Electrical Engineering,Guwahati,India
2. Linkoping University,Department of Electrical Engineering (ISY),Linköping,Sweden
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9833808/9833812/09833926.pdf?arnumber=9833926
Reference19 articles.
1. Adversarial Attacks on Deep-Learning Based Radio Signal Classification
2. Robust Adversarial Attacks Against DNN-Based Wireless Communication Systems
3. SafeAMC: Adversarial training for robust modulation recognition models;maroto,2021
4. Countermeasures Against Adversarial Examples in Radio Signal Classification
5. Learning with a strong adversary;huang,2016
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3. Wireless Universal Adversarial Attack and Defense for Deep Learning-Based Modulation Classification;IEEE Communications Letters;2024-03
4. Input Agnostic Trojan Attack for Deep Learning-Based Wireless Signal Classification;2024 National Conference on Communications (NCC);2024-02-28
5. Defending Wireless Receivers Against Adversarial Attacks on Modulation Classifiers;IEEE Internet of Things Journal;2023-11-01
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