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Recently, many deep neural networks (DNN) based modulation classificationschemes have been proposed in the literature. We have evaluated the robustnessof two famous such modulation classifiers (based on the techniques ofconvolutional neural networks and long short term memory) against adversarialmachine learning attacks in black-box settings. We have used Carlini \& Wagner(C-W) attack for performing the adversarial attack. To the best of ourknowledge, the robustness of these modulation classifiers has not beenevaluated through C-W attack before. Our results clearly indicate thatstate-of-art deep machine learning-based modulation classifiers are not robustagainst adversarial attacks.

Black-box Adversarial ML Attack on Modulation Classification