A Robust Few-Shot SEI Method Using Class-Reconstruction and Adversarial Training

Abstract

Specific emitter identification (SEI) is a promising physical layer authentication technique based on unintentionally hardware impairments of transmitters. These impairments are independent of the data’s content, so they are difficult to forge and analyze. Recently, most deep learning (DL) based SEI methods have been proposed, and have shown their great performance. However, these methods are big data-driven which means they have poor performance with limited training samples, and the vulnerability of neural networks to adversarial attacks is also a problem worth considering. In this paper, we propose an innovative few-shot SEI method based on class-reconstruction classification network and adversarial training (CRCN-AT) without the support of auxiliary dataset. Simulation results show that the proposed method achieves better identification performance and robustness in few-shot scenarios compared to traditional methods. The Pytorch code is released at https://github.comLIUC-000/CRCN-AT.