Ambient Light Reflection-Based Eavesdropping Enhanced With cGAN

Sound eavesdropping using light has been an area of considerable interest and concern, as it can be achieved over long distances. However, previous work has often lacked stealth (e.g., active emission of laser beams) or been limited in the range of realistic applications (e.g., using direct light from a device’s indicator LED or a hanging light bulb). In this paper, we present EchoLight , a non-intrusive, passive and long-range sound eavesdropping method that utilizes the extensive reflection of ambient light from vibrating objects to reconstruct sound. We analyze the relationship between reflection light signals and sound signals, particularly in situations where the frequency response of reflective objects and the efficiency of diffuse reflection are suboptimal. Based on this analysis, we have introduced an algorithm based on cGAN to address the issues of nonlinear distortion and spectral absence in the frequency domain of sound. We extensively evaluate EchoLight ’s performance in a variety of real-world scenarios. It demonstrates the ability to accurately reconstruct audio from a variety of source distances, attack distances, sound levels, light sources, and reflective materials. Our results reveal that the reconstructed audio exhibits a high degree of similarity to the original audio over 40 meters of attack distance.