Adversarial attacks and defenses for digital communication signals identification  

作　　者：Qiao Tian Sicheng Zhang Shiwen Mao Yun Lin 

机构地区：[1]College of Computer Science and Technology,Harbin Engineering University,Harbin,150001,China [2]College of Information and Communication Engineering,Harbin Engineering University,Harbin,150000,China [3]Department of Electrical and Computer Engineering,Auburn University,Auburn,AL,36849,USA

出　　处：《Digital Communications and Networks》2024年第3期756-764,共9页数字通信与网络（英文版）

基　　金：supported by the National Natural Science Foundation of China(61771154);the Fundamental Research Funds for the Central Universities(3072022CF0601);supported by Key Laboratory of Advanced Marine Communication and Information Technology,Ministry of Industry and Information Technology,Harbin Engineering University,Harbin,China.

摘　　要：As modern communication technology advances apace,the digital communication signals identification plays an important role in cognitive radio networks,the communication monitoring and management systems.AI has become a promising solution to this problem due to its powerful modeling capability,which has become a consensus in academia and industry.However,because of the data-dependence and inexplicability of AI models and the openness of electromagnetic space,the physical layer digital communication signals identification model is threatened by adversarial attacks.Adversarial examples pose a common threat to AI models,where well-designed and slight perturbations added to input data can cause wrong results.Therefore,the security of AI models for the digital communication signals identification is the premise of its efficient and credible applications.In this paper,we first launch adversarial attacks on the end-to-end AI model for automatic modulation classifi-cation,and then we explain and present three defense mechanisms based on the adversarial principle.Next we present more detailed adversarial indicators to evaluate attack and defense behavior.Finally,a demonstration verification system is developed to show that the adversarial attack is a real threat to the digital communication signals identification model,which should be paid more attention in future research.

关 键 词：Digital communication signals identification AI model Adversarial attacks Adversarial defenses Adversarial indicators 

分 类 号：TN91[电子电信—通信与信息系统]