Wireless-Tap: Automatic Transcription of Phone Calls Using Millimeter-Wave Radar Sensing

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.