Mixture GAN For Modulation Classification Resiliency Against Adversarial Attacks
Author:
Affiliation:
1. University of Houston,Electrical and Computer Engineering Department,Houston,TX,USA
2. University of Houston,Department of Engineering Technology,Houston,TX,USA
Funder
Air Force Research Laboratory (AFRL)
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10000063/10000593/10001138.pdf?arnumber=10001138
Reference22 articles.
1. Ensemble adversarial training: Attacks and defenses;tramèr;International Conference on Learning Representations,0
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4. Early methods for detecting adversarial images;hendrycks;International Conference on Learning Representations,2017
5. Explaining and harnessing adversarial examples;goodfellow;ArXiv Preprint,2014
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4. Implementation of Real-Time Adversarial Attacks on DNN-based Modulation Classifier;2023 International Conference on Computing, Networking and Communications (ICNC);2023-02-20
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