How to Make 5G Communications" Invisible": Adversarial Machine Learning for Wireless Privacy
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 …
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 …
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