We consider a wireless communication system that consists of a transmitter, a receiver, and
an adversary. The transmitter transmits signals with different modulation types, while 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 …

Deep learning-based automatic modulation classification (AMC) models are susceptible to
adversarial attacks. Such attacks inject specifically crafted wireless interference into …

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 …

Deep learning (DL) models are vulnerable to adversarial attacks, by adding a subtle
perturbation which is imperceptible to the human eye, a convolutional neural network (CNN) …

We consider a communication scenario, in which an intruder tries to determine the
modulation scheme of the intercepted signal. Our aim is to minimize the accuracy of the …

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

The discovery of adversarial examples poses a serious risk to the deep neural networks
(DNN). By adding a subtle perturbation that is imperceptible to the human eye, a well …

Advances in adversarial attack and defense technologies will enhance the reliability of deep
learning (DL) systems spirally. Most existing adversarial attack methods make overly ideal …