RadEar: A Self-Supervised RF Backscatter System for Voice Eavesdropping and Separation

Abstract

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.

Figures (9)

• Figure 1: Threat model and system configuration.
• Figure 2: Diagram of our RF backscatter tag.
• Figure 3: Diagram of our RF reader design.
• Figure 4: Self-supervised training of audio separation model.
• Figure 5: Self-supervised training of audio denoising model.