{"title":"TagMic:通过RFID信号收听","authors":"Yin Li, Chunhui Duan, Xuan Ding, Cihang Liu","doi":"10.1109/ICDCS47774.2020.00136","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"177 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"TagMic: Listening Through RFID Signals\",\"authors\":\"Yin Li, Chunhui Duan, Xuan Ding, Cihang Liu\",\"doi\":\"10.1109/ICDCS47774.2020.00136\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":158630,\"journal\":{\"name\":\"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)\",\"volume\":\"177 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDCS47774.2020.00136\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDCS47774.2020.00136","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.