Detecting Hidden Voice Recorders via ADC Electromagnetic Radiation

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.

Authors

Institutions

Publication Details

Journal
ACM Transactions on Sensor Networks
Published
2024-10-22
DOI
https://doi.org/10.1145/3700595
Citations
2
Primary Topic
Speech and Audio Processing
Type
article
Field-Weighted Citation Impact
0.70

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Detecting Hidden Voice Recorders via ADC Electromagnetic Radiation

Ruochen Zhou, Xiaoyu Ji, Wenyuan Xu, Chen Yan et al.
2 citations
ACM Transactions on Sensor Networks
Speech and Audio Processing
0.70
article

Detecting Hidden Voice Recorders via ADC Electromagnetic Radiation

Ruochen Zhou, Xiaoyu Ji, Wenyuan Xu, Chen Yan, Han Chen
article en
2 citations

Abstract

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.

ACM Transactions on Sensor NetworksVol. 20(6)
Zhejiang University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 30%
Speech and Audio Processing
0.70
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.