Side Eye: Characterizing the Limits of POV Acoustic Eavesdropping from Smartphone Cameras with Rolling Shutters and Movable Lenses

TL;DR

This paper investigates how smartphone camera hardware, especially rolling shutters and movable lenses, unintentionally leak acoustic information through image distortions, enabling a novel POV side-channel eavesdropping method without line of sight.

Contribution

It characterizes the physical limits of acoustic leakage via smartphone cameras and introduces a signal processing pipeline to recognize leaked sounds with high accuracy.

Findings

Achieved up to 99.67% accuracy in recognizing spoken digits.

Demonstrated the side channel's effectiveness across 10 different smartphones.

Identified hardware behaviors that amplify acoustic information leakage.

Abstract

Our research discovers how the rolling shutter and movable lens structures widely found in smartphone cameras modulate structure-borne sounds onto camera images, creating a point-of-view (POV) optical-acoustic side channel for acoustic eavesdropping. The movement of smartphone camera hardware leaks acoustic information because images unwittingly modulate ambient sound as imperceptible distortions. Our experiments find that the side channel is further amplified by intrinsic behaviors of Complementary metal-oxide-semiconductor (CMOS) rolling shutters and movable lenses such as in Optical Image Stabilization (OIS) and Auto Focus (AF). Our paper characterizes the limits of acoustic information leakage caused by structure-borne sound that perturbs the POV of smartphone cameras. In contrast with traditional optical-acoustic eavesdropping on vibrating objects, this side channel requires no line of sight and no object within the camera's field of view (images of a ceiling suffice). Our experiments test the limits of this side channel with a novel signal processing pipeline that extracts and recognizes the leaked acoustic information. Our evaluation with 10 smartphones on a spoken digit dataset reports 80.66%, 91.28%, and 99.67% accuracies on recognizing 10 spoken digits, 20 speakers, and 2 genders respectively. We further systematically discuss the possible defense strategies and implementations. By modeling, measuring, and demonstrating the limits of acoustic eavesdropping from smartphone camera image streams, our contributions explain the physics-based causality and possible ways to reduce the threat on current and future devices.