Genglin Wang;Zheng Shi;Yanni Yang;Zhenlin An;Guoming Zhang;Pengfei Hu;Xiuzhen Cheng;Jiannong Cao
{"title":"Wireless Eavesdropping on Wired Audio With Radio-Frequency Retroreflector Attack","authors":"Genglin Wang;Zheng Shi;Yanni Yang;Zhenlin An;Guoming Zhang;Pengfei Hu;Xiuzhen Cheng;Jiannong Cao","doi":"10.1109/TMC.2024.3505268","DOIUrl":null,"url":null,"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, <small>RF-Parrot</small><inline-formula><tex-math>${}^{\\mathbf {2}}$</tex-math></inline-formula>, 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.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"3178-3195"},"PeriodicalIF":7.7000,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Mobile Computing","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10766401/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
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.
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.