Effects of Forward Error Correction on Communications Aware Evasion Attacks
Matthew DelVecchio, Bryse Flowers, William C. Headley

TL;DR
This paper enhances a communications-aware adversarial attack framework by integrating forward error correction (FEC) coding, improving attack performance and balancing evasion with successful communication in RFML systems.
Contribution
It introduces new loss functions and design considerations that incorporate FEC knowledge, advancing the effectiveness of adversarial attacks in noisy environments.
Findings
FEC coding improves attack performance even without explicit knowledge.
Enhanced framework better balances evasion and communication success.
Performance surpasses prior methods in adverse conditions.
Abstract
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 attacks have been shown to be successful in disrupting the performance of an eavesdropper, they fail to fully support the primary goal of successful intended communication. To remedy this, a communications-aware attack framework was recently developed that allows for a more effective balance between the opposing goals of evasion and intended communication through the novel use of a DNN to intelligently create the adversarial communication signal. Given the near ubiquitous usage of forward error correction (FEC) coding in the majority of deployed systems to correct errors that arise, incorporating FEC in this framework is a natural extension of this prior work and will allow for improved performance in more adverse…
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