计算机科学 ›› 2023, Vol. 50 ›› Issue (6A): 220200151-6.doi: 10.11896/jsjkx.220200151
余加宝1, 姚俊梅1, 谢瑞桃1, 伍楷舜1, 马军超2
YU Jiabao1, YAO Junmei1, XIE Ruitao1, WU Kaishun1, MA Junchao2
摘要： 无线射频识别(Radio Frequency Identification,RFID)系统最基本的功能是标签识别,然而身份验证系统无法检测到伪造或克隆标签,从而出现潜在安全隐患和个人隐私问题。目前有基于加密的认证协议和基于特征提取的解决方法,其中基于加密的认证协议方法不兼容现有的协议,基于特征提取的方法存在特征提取困难或者识别距离短等限制。文中基于标签物理层信号的真实性进行识别,结合深度学习技术,提出标签信号识别方法。其核心思想在于在RFID通信过程中,利用标签的后向散射信号提取与标签逻辑信息无关的信号,将提取的信号送入卷积神经网络进行相似度匹配,根据得到的相似度匹配分数与给定的阈值对比,最后实现标签的真实性识别。采用USRP N210作为RFID系统的阅读器,采用150个超高频商用标签作为信号的发射器,并在实际场景中采集真实的RFID信号。通过实验验证了基于深度学习的标签识别能达到94%以上的识别精度,在识别距离长达2m的情况下其等错误比率(EER)为0.034。
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