| Autor: |
Zhou, Ruochen, Ji, Xiaoyu, Chen, Han, Yan, Chen, Xu, Wenyuan |
| Předmět: |
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| Zdroj: |
ACM Transactions on Sensor Networks; Nov2024, Vol. 20 Issue 6, p1-26, 26p |
| Abstrakt: |
Unauthorized covert voice recording presents a significant threat to privacy-sensitive scenarios, such as confidential meetings and private conversations. Due to their miniaturization and disguise characteristics, hidden voice recorders are difficult to notice. In this article, we present DeHiREC, the first proof-of-concept system capable of detecting offline hidden voice recorders from their electromagnetic radiations (EMR). We first characterize the unique patterns of the emanated EMR signals and then locate the EMR source, i.e., the analog-to-digital converter module embedded in the mixed signal system-on-chips. Since these unintentional EMR signals can be extremely noisy and weak, accurately detecting them can be challenging. To address this challenge, we design an EMR Catalyzing method to actively stimulate the EMR signals and then employ an adaptive-folding algorithm to improve the signal-to-noise ratio of the sensed EMRs. We evaluate the performance of DeHiREC on 18 commercial voice recorders under various impacts, including interference from other devices. Experimental results reveal that DeHiREC is effective in detecting all 18 voice recorders and achieves an overall success rate of 94.72% and a recall rate of 92.03% at a distance of 0.2 m. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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