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

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

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

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 …

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 …

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

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

Wireless communications has greatly benefited in recent years from advances in machine
learning. A new subfield, commonly termed Radio Frequency Machine Learning (RFML) …

Recent advancements in radio frequency machine learning (RFML) have demonstrated the
use of raw in-phase and quadrature (IQ) samples for multiple spectrum sensing tasks. Yet …

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 …