There is significant enthusiasm for the employment of Deep Neural Networks (DNNs) for
important tasks in major wireless communication systems: channel estimation and decoding …

Deep learning (DL), despite its enormous success in many computer vision and language
processing applications, is exceedingly vulnerable to adversarial attacks. We consider the …

We consider a wireless communication system, where a transmitter sends signals to a
receiver with different modulation types while the receiver classifies the modulation types of …

This paper presents channel-aware adversarial attacks against deep learning-based
wireless signal classifiers. There is a transmitter that transmits signals with different …

Recent work in adversarial radio frequency machine learning has demonstrated the use of
untargeted adversarial machine learning techniques for over the air evasion of raw inphase …

We consider a wireless communication system that consists of a background emitter, a
transmitter, and an adversary. The transmitter is equipped with a deep neural network (DNN) …

We show that end-to-end learning of communication systems through deep neural network
autoencoders can be extremely vulnerable to physical adversarial attacks. Specifically, we …

Adversarial attacks and defenses in machine learning and deep neural network (DNN) have
been gaining significant attention due to the rapidly growing applications of deep learning in …

Adversarial examples in machine learning for images are widely publicized and explored.
Illustrations of misclassifications caused by these slightly perturbed inputs are abundant and …

With the emergence of the information age, mobile data has become more random,
heterogeneous and massive. Thanks to its many advantages, deep learning is increasingly …