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TagMic: Listening Through RFID Signals

机译：Tagmic：通过RFID信号侦听

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摘要

RFID is an increasingly ubiquitous technology widely adopted in both the industry and our daily life nowadays. But when it comes to eavesdropping, people usually pay attention to devices like cameras and mobile phones, instead of small-volume and battery-free RFID tags. This work shows the possibility of using prevalence RFIDs to capture and recognize the acoustic signals. To be specific, we attach an RFID tag on an object, which is located in the vicinity of the sound source. Our key innovation lies in the translation between the vibrations induced when the sound wave hits the object surface and the fluctuations in the tag’s RF signals. Although the inherent sampling rate of commercial RFID devices is deficient, and the vibrations are very subtle, we still extract characteristic features from imperfect measurements by taking advantage of state-of-the-art machine learning and signal processing algorithms. We have implemented our system with commercial RFID and loudspeaker equipment and evaluated it intensively in our lab environment. Experimental results show that the average success rate in detecting single tone sounds can reach as high as 93.10%. We believe our work would raise the attention of RFID in the concern of surveillance and security.
机译：RFID是在行业和日常生活中被广泛采用的越来越普遍的技术。但是在窃听时，人们通常会注意像相机和手机等设备，而不是小容量和无电池RFID标签。这项工作显示了使用普遍存在RFID捕获和识别声学信号的可能性。具体而言，我们在对象上附加一个RFID标签，该标签位于声源附近。我们的关键创新在于在声波击中物体表面和标签RF信号中的波动时所引起的振动之间的翻译。虽然商业RFID设备的固有采样率缺乏，但振动非常微妙，我们仍然利用最先进的机器学习和信号处理算法来提取来自不完美测量的特征特征。我们已使用商业RFID和扬声器设备实施我们的系统，并在我们的实验室环境中进行了集中评估。实验结果表明，检测单音声音的平均成功率可以高达93.10％。我们相信我们的工作将提高RFID在监督和安全的关注中的注意。

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