EM Eye: Characterizing Electromagnetic Side-channel Eavesdropping on Embedded Cameras

Abstract

IoT devices and other embedded systems are increasingly equipped with cameras that can sense critical information in private spaces. The data security of these cameras, however, has hardly been scrutinized from the hardware design perspective. Our paper presents the first attempt to analyze the attack surface of physical-channel eavesdropping on embedded cameras. We characterize EM Eye-a vulnerability in the digital image data transmission interface that allows adversaries to reconstruct high-quality image streams from the cameras' unintentional electromagnetic emissions, even from over 2 meters away in many cases. Our evaluations of 4 popular IoT camera development platforms and 12 commercial off-the-shelf devices with cameras show that EM Eye poses threats to a wide range of devices, from smartphones to dash cams and home security cameras. By exploiting this vulnerability, adversaries may be able to visually spy on private activities in an enclosed room from the other side of a wall. We provide root cause analysis and modeling that enable system defenders to identify and simulate mitigation against this vulnerability, such as improving embedded cameras' data transmission protocols with minimum costs. We further discuss EM Eye's relationship with known computer display eavesdropping attacks to reveal the gaps that need to be addressed to protect the data confidentiality of sensing systems.