Communication without Interception: Defense against Modulation Detection

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 constellation perturbation 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 a legitimate receiver that is oblivious to the perturbation. Simulation results demonstrate the viability of our approach to make wireless communication secure against both state-of-the-art deep-learning- and decision-tree-based intruders with minimal sacrifice in the communication performance.