  Deep learning-based automatic modulation classification (AMC) models are
susceptible to adversarial attacks. Such attacks inject specifically crafted
(non-random) wireless interference into transmitted signals to induce erroneous
classification predictions. Furthermore, adversarial interference is
transferable in black box environments, allowing an adversary to attack
multiple deep learning models with a single perturbation crafted for a
particular classification model. In this work, we propose a novel wireless
receiver architecture to mitigate the effects of adversarial interference in
various black box attack environments. We begin by evaluating the architecture
uncertainty environment, where we show that adversarial attacks crafted to fool
specific AMC DL architectures are not directly transferable to different DL
architectures. Next, we consider the domain uncertainty environment, where we
show that adversarial attacks crafted on time domain and frequency domain
features to not directly transfer to the altering domain. Using these insights,
we develop our Assorted Deep Ensemble (ADE) defense, which is an ensemble of
deep learning architectures trained on time and frequency domain
representations of received signals. Through evaluation on two wireless signal
datasets under different sources of uncertainty, we demonstrate that our ADE
obtains substantial improvements in AMC classification performance compared
with baseline defenses across different adversarial attacks and potencies.
