38 citations · 71 across the 4 of their papers we have counts for
3 papers · 1 filter
The RFML Ecosystem: A Look at the Unique Challenges of Applying Deep Learning to Radio Frequency Applications
Lauren J. Wong, William H. Clark, Bryse Flowers +3
While deep machine learning technologies are now pervasive in state-of-the-art image recognition and natural language processing applications, only in recent years have these techn…
Effects of Forward Error Correction on Communications Aware Evasion Attacks
Matthew DelVecchio, Bryse Flowers, William C. Headley
Recent work has shown the impact of adversarial machine learning on deep neural networks (DNNs) developed for Radio Frequency Machine Learning (RFML) applications. While these atta…
Evaluating Adversarial Evasion Attacks in the Context of Wireless Communications
Bryse Flowers, R. Michael Buehrer, William C. Headley
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, deep…