  We consider the problem of hiding wireless communications from an
eavesdropper that employs a deep learning (DL) classifier to detect whether any
transmission of interest is present or not. There exists one transmitter that
transmits to its receiver in the presence of an eavesdropper, while a
cooperative jammer (CJ) transmits carefully crafted adversarial perturbations
over the air to fool the eavesdropper into classifying the received
superposition of signals as noise. The CJ puts an upper bound on the strength
of perturbation signal to limit its impact on the bit error rate (BER) at the
receiver. We show that this adversarial perturbation causes the eavesdropper to
misclassify the received signals as noise with high probability while
increasing the BER only slightly. On the other hand, the CJ cannot fool the
eavesdropper by simply transmitting Gaussian noise as in conventional jamming
and instead needs to craft perturbation signals built by adversarial machine
learning to enable covert communications. Our results show that signals with
different modulation types and eventually 5G communications can be effectively
hidden from an eavesdropper even if it is equipped with a DL classifier to
detect transmissions.
