2025-08-08 ペンシルベニア州立大学(PennState)
A team of computer science researchers used a millimeter-wave radar sensor to collect conversations from the vibrations of smartphones and adapted an open-access, large-scale artificial intelligence-integrated speech recognition model, to decode the vibrations into recognizable speech transcriptions. Credit: iStock/martin-dm. All Rights Reserved.
＜関連情報＞
- https://www.psu.edu/news/engineering/story/conversations-remotely-detected-cellphone-vibrations-researchers-report
- https://dl.acm.org/doi/abs/10.1145/3734477.3734708
ワイヤレスタップ：ミリ波レーダーセンシングを用いた電話通話の自動転写 Wireless-Tap: Automatic Transcription of Phone Calls Using Millimeter-Wave Radar Sensing
Suryoday Basak, Mahanth Gowda
WiSec 2025: 18th ACM Conference on Security and Privacy in Wireless and Mobile Networks Published: 30 June 2025
DOI:https://doi.org/10.1145/3734477.3734708
Abstract
This paper presents WirelessTap, a system that demonstrates the potential for automated speech recognition (ASR) on phone call audio eavesdropped remotely using commercially available frequency modulated continuous wave millimeter-wave (mmWave) radars operating in the 77-81 GHz range. WirelessTap detects minute vibrations from smartphone earpieces, converts them into audio, and processes this audio for speech transcription. This work presents the first full-sentence ASR using mmWave radars on earpiece vibrations using a 10,000-word vocabulary, achieving a 300 cm attack range across multiple smartphone models. It surpasses prior radar-based eavesdropping studies limited to loudspeakers, small vocabularies, or constrained evaluations. To address challenges like the absence of large mmWave radar-based audio datasets, low signal-to-noise ratio, and limited voice frequency ranges extractable from radar data, WirelessTap incorporates synthetic data generation, domain adaptation, and inference using OpenAI’s Whisper ASR model. Our experiments systematically show how word accuracy rate gradually decreases with distance, from as high as 59.25% at 50 cm to 2% at 300 cm; additionally, we deploy this attack to a real-world setting with a user study targeting a victim holding a smartphone to their ear. This paper highlights the evolving risks of artificial intelligence and sensor systems being misused as technology advances.
