  We consider a communication scenario, in which an intruder tries to determine
the modulation scheme of the intercepted signal. Our aim is to minimize the
accuracy of the intruder, while guaranteeing that the intended receiver can
still recover the underlying message with the highest reliability. This is
achieved by perturbing channel input symbols at the encoder, similarly to
adversarial attacks against classifiers in machine learning. In image
classification, the perturbation is limited to be imperceptible to a human
observer, while in our case the perturbation is constrained so that the message
can still be reliably decoded by the legitimate receiver, which is oblivious to
the perturbation. Simulation results demonstrate the viability of our approach
to make wireless communication secure against state-of-the-art intruders (using
deep learning or decision trees) with minimal sacrifice in the communication
performance. On the other hand, we also demonstrate that using diverse training
data and curriculum learning can significantly boost the accuracy of the
intruder.
