  Deep learning algorithms have been shown to be powerful in many communication
network design problems, including that in automatic modulation classification.
However, they are vulnerable to carefully crafted attacks called adversarial
examples. Hence, the reliance of wireless networks on deep learning algorithms
poses a serious threat to the security and operation of wireless networks. In
this letter, we propose for the first time a countermeasure against adversarial
examples in modulation classification. Our countermeasure is based on a neural
rejection technique, augmented by label smoothing and Gaussian noise injection,
that allows to detect and reject adversarial examples with high accuracy. Our
results demonstrate that the proposed countermeasure can protect deep-learning
based modulation classification systems against adversarial examples.
