Covert communication method based on tripartite generative adversarial network

Abstract

A novel tripartite generative adversarial network (TripartiteGAN) and a covert communication method based on TripartiteGAN were designed for jointly optimizing the transmission covertness and the demodulation accuracy of the covert message.The performance of the method was analyzed.Specifically, TripartiteGAN was used to manipulate the amplitude and phase of an input modulated covert data so that the distribution of the generated covert signal superposing the overt signal approximates to that of the overt signal for the public user.The proposed method could work with an optimum warden that needs neither to set the detection threshold manually nor to know the transmit power characteristics of the sender.Simulation results show that under the additive white Gaussian noise channel, the proposed TripartiteGAN improved the demodulation accuracy at the covert receiver end while keeping the probability of regarding the detected signal as the covert one or the overt one at the warden around 0.5.Moreover, the proposed method outperforms the existing covert communication scheme based on generative adversarial network (GAN).

Keywords

• wireless communication;generative adversarial network;covert communication;machine learning