  Automatic modulation classification can be a core component for intelligent
spectrally efficient wireless communication networks, and deep learning
techniques have recently been shown to deliver superior performance to
conventional model-based strategies, particularly when distinguishing between a
large number of modulation types. However, such deep learning techniques have
also been recently shown to be vulnerable to gradient-based adversarial attacks
that rely on subtle input perturbations, which would be particularly feasible
in a wireless setting via jamming. One such potent attack is the one known as
the Carlini-Wagner attack, which we consider in this work. We further consider
a data-driven subsampling setting, where several recently introduced
deep-learning-based algorithms are employed to select a subset of samples that
lead to reducing the final classifier's training time with minimal loss in
accuracy. In this setting, the attacker has to make an assumption about the
employed subsampling strategy, in order to calculate the loss gradient. Based
on state of the art techniques available to both the attacker and defender, we
evaluate best strategies under various assumptions on the knowledge of the
other party's strategy. Interestingly, in presence of knowledgeable attackers,
we identify computational cost reduction opportunities for the defender with no
or minimal loss in performance.
