Universal Attack Against Automatic Modulation Classification DNNs Under Frequency and Data Constraints
Author:
Affiliation:
1. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China
2. 63rd Research Institute, National University of Defense Technology, Nanjing, China
Funder
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing
Link
http://xplorestaging.ieee.org/ielx7/6488907/10175002/10070369.pdf?arnumber=10070369
Reference41 articles.
1. Adversarial Attacks with Multiple Antennas Against Deep Learning-Based Modulation Classifiers
2. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
3. Targeted Adversarial Examples Against RF Deep Classifiers
4. Generating Adversarial Examples with Adversarial Networks
5. Position-Invariant Adversarial Attacks on Neural Modulation Recognition
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1. Waveform Manipulation Against DNN-based Modulation Classification Attacks;MILCOM 2023 - 2023 IEEE Military Communications Conference (MILCOM);2023-10-30
2. Sparse Adversarial Attacks against DL-Based Automatic Modulation Classification;Electronics;2023-09-05
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