We consider a communication scenario, in which an intruder, employing a deep neural network (DNN), 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 DNN-based classifiers. In the latter perturbation is limited to be imperceptible to a human observer, while in our case 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 DNN-based intruders with minimal sacrifice in the communication performance.

本文提出了一种抵御针对调制方案进行攻击的无线通信保密方法，通过扰动信道输入符号来达到识别攻击者并保障合法接收端的目的。仿真结果表明，该方法在保证通信效果的同时，能有效防御基于深度学习或决策树的攻击，并证明了多样化的训练数据和课程学习能显著提高攻击准确率。

攻击是最好的防御：对抗性攻击以避免调制检测