  Deep learning based automatic modulation classification (AMC) has received
significant attention owing to its potential applications in both military and
civilian use cases. Recently, data-driven subsampling techniques have been
utilized to overcome the challenges associated with computational complexity
and training time for AMC. Beyond these direct advantages of data-driven
subsampling, these methods also have regularizing properties that may improve
the adversarial robustness of the modulation classifier. In this paper, we
investigate the effects of an adversarial attack on an AMC system that employs
deep learning models both for AMC and for subsampling. Our analysis shows that
subsampling itself is an effective deterrent to adversarial attacks. We also
uncover the most efficient subsampling strategy when an adversarial attack on
both the classifier and the subsampler is anticipated.
