Deep learning techniques can classify spectrum phenomena (eg, waveform modulation)
with accuracy levels that were once thought impossible. Although we have recently seen …

Adversarial examples (AdExs) in machine learning for classification of radio frequency (RF)
signals can be created in a targeted manner such that they go beyond general …

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

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 is increasingly being used for many tasks in wireless communications, such
as modulation classification. However, it has been shown to be vulnerable to adversarial …

Deep learning algorithms have been shown to be powerful in many communication network
design problems, including that in automatic modulation classification. However, they are …

Machine learning (ML) provides effective means to learn from spectrum data and solve
complex tasks involved in wireless communications. Supported by recent advances in …

Motivated by the superior performance of deep learning in many applications including
computer vision and natural language processing, several recent studies have focused on …

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

Deep learning (DL) is a powerful technique for many real-time applications, but it is
vulnerable to adversarial attacks. Herein, we consider DL-based modulation classification …