Mitigation of Adversarial Examples in RF Deep Classifiers Utilizing AutoEncoder Pre-training
This addresses a critical security issue for RF-based communications and radar sensing systems, but it is incremental as it adapts known image defense techniques to the RF domain.
The paper tackles the problem of adversarial examples in deep learning for radio frequency (RF) signals, which can cause targeted misclassifications like mistaking BPSK for 8-PSK, and proposes a defense using autoencoder pre-training, validating it as a viable mitigation method.
Adversarial examples in machine learning for images are widely publicized and explored. Illustrations of misclassifications caused by slightly perturbed inputs are abundant and commonly known (e.g., a picture of panda imperceptibly perturbed to fool the classifier into incorrectly labeling it as a gibbon). Similar attacks on deep learning (DL) for radio frequency (RF) signals and their mitigation strategies are scarcely addressed in the published work. Yet, RF adversarial examples (AdExs) with minimal waveform perturbations can cause drastic, targeted misclassification results, particularly against spectrum sensing/survey applications (e.g. BPSK is mistaken for 8-PSK). Our research on deep learning AdExs and proposed defense mechanisms are RF-centric, and incorporate physical world, over-the-air (OTA) effects. We herein present defense mechanisms based on pre-training the target classifier using an autoencoder. Our results validate this approach as a viable mitigation method to subvert adversarial attacks against deep learning-based communications and radar sensing systems.