Harnessing the Adversarial Perturbation to Enhance Security in the Autoencoder-Based Communication System

Abstract

Given the vulnerability of deep neural network to adversarial attacks, the application of deep learning in the wireless physical layer arouses comprehensive security concerns. In this paper, we consider an autoencoder-based communication system with a full-duplex (FD) legitimate receiver and an external eavesdropper. It is assumed that the system is trained from end-to-end based on the concepts of autoencoder. The FD legitimate receiver transmits a well-designed adversary perturbation signal to jam the eavesdropper while receiving information simultaneously. To defend the self-perturbation from the loop-back channel, the legitimate receiver is re-trained with the adversarial training method. The simulation results show that with the scheme proposed in this paper, the block-error-rate (BLER) of the legitimate receiver almost remains unaffected while the BLER of the eavesdropper is increased by orders of magnitude. This ensures reliable and secure transmission between the transmitter and the legitimate receiver.