RadEar: A Self-Supervised RF Backscatter System for Voice Eavesdropping and Separation
This addresses privacy and security threats by enabling eavesdropping without ground-truth labels, though it is incremental in applying self-supervised learning to this domain.
The paper tackles the problem of covert voice eavesdropping through walls using an RF backscatter system, achieving high-fidelity speech recovery and separation in real-world scenarios.
Eavesdropping on voice conversations presents a growing threat to personal privacy and information security. In this paper, we present RadEar, a novel RF backscatter-based system designed to enable covert voice eavesdropping through walls. RadEar consists of two key components: (i) a batteryless RF backscatter tag covertly deployed inside the target space, and (ii) an RF reader located outside the room that performs signal demodulation, voice separation, and denoising. The tag features a compact, dual-resonator design that achieves energy-efficient frequency modulation for continuous voice eavesdropping while mitigating self-interference by separating excitation and reflection frequencies. To overcome the challenges of weak signal reception and overlapping speech, the RF reader employs self-supervised learning models for voice separation and denoising, trained using a remix-based objective without requiring ground-truth labels. We fabricate and evaluate RadEar in real-world scenarios, demonstrating its ability to recover and separate human speech with high fidelity under practical constraints.