Adversarial Robustness of Distilled and Pruned Deep Learning-based Wireless Classifiers

Data-driven deep learning (DL) techniques developed for automatic modulation classification (AMC) of wireless signals are vulnerable to adversarial attacks. This poses a severe security threat to the DL-based wireless systems, specifically for edge applications of AMC. In this work, we address the j...

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Hauptverfasser:	Baishya, Nayan Moni, Manoj, B. R
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Cryptography and Security Computer Science - Information Theory Computer Science - Learning Mathematics - Information Theory Statistics - Machine Learning
Online-Zugang:	Volltext bestellen
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