  Deep learning (DL), despite its enormous success in many computer vision and
language processing applications, is exceedingly vulnerable to adversarial
attacks. We consider the use of DL for radio signal (modulation) classification
tasks, and present practical methods for the crafting of white-box and
universal black-box adversarial attacks in that application. We show that these
attacks can considerably reduce the classification performance, with extremely
small perturbations of the input. In particular, these attacks are
significantly more powerful than classical jamming attacks, which raises
significant security and robustness concerns in the use of DL-based algorithms
for the wireless physical layer.
