Recent studies have demonstrated the feasibility of eavesdropping on audio via radio frequency signals or videos, which capture physical surface vibrations from surrounding objects. However, these methods are inadequate for intercepting internally transmitted audio through wired media. In this work, we introduce radio-frequency retroreflector attack (RFRA) and bridge this gap by proposing an RFRA-based eavesdropping system,RF-Parrot${}^{\mathbf {2}}$, capable of wirelessly capturing audio signals transmitted through earphone wires. Our system entails embedding a tiny field-effect transistor within the wire to establish a battery-free retroreflector, whose reflective efficiency is correlated with the amplitude of the audio signal. To preserve the details of audio signals, we designed a unique retroreflector using a depletion-mode MOSFET (D-MOSFET). This MOSFET can be triggered by any voltage level present in the audio signals, thus guaranteeing no information loss during activation. However, the D-MOSFET introduces a nonlinear convolution operation on the original audio, resulting in distorted audio eavesdropping. Thus, we devised an engineering solution which utilized a novel convolutional neural network in conjunction with an efficient Parallel WaveGAN vocoder to reconstruct the original audio. Our comprehensive experiments demonstrate a strong similarity between the reconstructed audio and the original, achieving an impressive 95% accuracy in speech command recognition.

Recent works demonstrated that we can eavesdrop on audio by using radio frequency signals or videos to capture the physical surface vibrations of surrounding objects. They fall short when it comes to intercepting internally transmitted audio through wires. In this work, we first address this gap by proposing a new eavesdropping system, RF-Parrot, that can wirelessly capture the audio signal transmitted in earphone wires. Our system involves embedding a tiny field-effect transistor in the wire to create a battery-free retroreflector, with its reflective efficiency tied to the audio signal’s amplitude. To capture full details of the analog audio signals, we engineered a novel retroreflector using a depletion-mode MOSFET, which can be activated by any voltage of the audio signals, ensuring no information loss. We also developed a theoretical model to demystify the nonlinear transmission of the retroreflector, identifying it as a convolution operation on the audio spectrum. Subsequently, we have designed a novel convolutional neural network-based model to accurately reconstruct the original audio. Our extensive experimental results demonstrate that the reconstructed audio bears a strong resemblance to the original audio, achieving an impressive 95% accuracy in speech command recognition.