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 …

Automatic modulation classification (AMC) is used in intelligent receivers operating in
shared spectrum environments to classify the modulation constellation of radio frequency …

Signal classification is a universal problem in adversarial wireless scenarios, especially
when an eavesdropping radio receiver attempts to glean information about a target …

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

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

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 …

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