Wireless Eavesdropping on Wired Audio With Radio-Frequency Retroreflector Attack

IF 7.7 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Genglin Wang;Zheng Shi;Yanni Yang;Zhenlin An;Guoming Zhang;Pengfei Hu;Xiuzhen Cheng;Jiannong Cao
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引用次数: 0

Abstract

Recent studies have demonstrated the feasibility of eavesdropping on audio via radio frequency signals or videos, which capture physical surface vibrations from surrounding objects. However, these methods are inadequate for intercepting internally transmitted audio through wired media. In this work, we introduce radio-frequency retroreflector attack (RFRA) and bridge this gap by proposing an RFRA-based eavesdropping system, RF-Parrot${}^{\mathbf {2}}$, capable of wirelessly capturing audio signals transmitted through earphone wires. Our system entails embedding a tiny field-effect transistor within the wire to establish a battery-free retroreflector, whose reflective efficiency is correlated with the amplitude of the audio signal. To preserve the details of audio signals, we designed a unique retroreflector using a depletion-mode MOSFET (D-MOSFET). This MOSFET can be triggered by any voltage level present in the audio signals, thus guaranteeing no information loss during activation. However, the D-MOSFET introduces a nonlinear convolution operation on the original audio, resulting in distorted audio eavesdropping. Thus, we devised an engineering solution which utilized a novel convolutional neural network in conjunction with an efficient Parallel WaveGAN vocoder to reconstruct the original audio. Our comprehensive experiments demonstrate a strong similarity between the reconstructed audio and the original, achieving an impressive 95% accuracy in speech command recognition.
利用射频反向反射器攻击对有线音频进行无线窃听
最近的研究已经证明了通过无线电频率信号或视频窃听音频的可行性,这些信号或视频可以捕获周围物体的物理表面振动。然而,这些方法不足以拦截通过有线媒体内部传输的音频。在这项工作中,我们引入了射频反反射攻击(RFRA),并通过提出一种基于RFRA的窃听系统RF-Parrot${}^{\mathbf{2}}$来弥补这一差距,该系统能够无线捕获通过耳机线传输的音频信号。我们的系统需要在电线内嵌入一个微小的场效应晶体管,以建立一个无电池的反向反射器,其反射效率与音频信号的振幅相关。为了保留音频信号的细节,我们设计了一个独特的反向反射器,使用耗尽模式MOSFET (D-MOSFET)。该MOSFET可以由音频信号中存在的任何电压电平触发,从而保证在激活期间没有信息丢失。然而,D-MOSFET在原始音频上引入了非线性卷积运算,导致音频窃听失真。因此,我们设计了一种工程解决方案,利用新颖的卷积神经网络结合高效的并行WaveGAN声码器来重建原始音频。我们的综合实验表明,重建的音频与原始音频之间具有很强的相似性,在语音命令识别中达到了令人印象深刻的95%的准确率。
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来源期刊
IEEE Transactions on Mobile Computing
IEEE Transactions on Mobile Computing 工程技术-电信学
CiteScore
12.90
自引率
2.50%
发文量
403
审稿时长
6.6 months
期刊介绍: IEEE Transactions on Mobile Computing addresses key technical issues related to various aspects of mobile computing. This includes (a) architectures, (b) support services, (c) algorithm/protocol design and analysis, (d) mobile environments, (e) mobile communication systems, (f) applications, and (g) emerging technologies. Topics of interest span a wide range, covering aspects like mobile networks and hosts, mobility management, multimedia, operating system support, power management, online and mobile environments, security, scalability, reliability, and emerging technologies such as wearable computers, body area networks, and wireless sensor networks. The journal serves as a comprehensive platform for advancements in mobile computing research.
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