They See Me Rollin': Inherent Vulnerability of the Rolling Shutter in CMOS Image Sensors

TL;DR

This paper reveals a vulnerability in CMOS image sensors' rolling shutter mechanism, demonstrating how bright light can be exploited to disrupt image capture, significantly impairing object detection systems.

Contribution

The study models, evaluates, and validates a novel rolling shutter attack method across various cameras, and proposes a lightweight detector enhancement to identify such attacks.

Findings

Adversaries can hide up to 75% of objects from detectors.

Common distortion metrics fail to detect the attack.

The attack is effective across diverse CMOS cameras.

Abstract

In this paper, we describe how the electronic rolling shutter in CMOS image sensors can be exploited using a bright, modulated light source (e.g., an inexpensive, off-the-shelf laser), to inject fine-grained image disruptions. We demonstrate the attack on seven different CMOS cameras, ranging from cheap IoT to semi-professional surveillance cameras, to highlight the wide applicability of the rolling shutter attack. We model the fundamental factors affecting a rolling shutter attack in an uncontrolled setting. We then perform an exhaustive evaluation of the attack's effect on the task of object detection, investigating the effect of attack parameters. We validate our model against empirical data collected on two separate cameras, showing that by simply using information from the camera's datasheet the adversary can accurately predict the injected distortion size and optimize their attack accordingly. We find that an adversary can hide up to 75% of objects perceived by state-of-the-art detectors by selecting appropriate attack parameters. We also investigate the stealthiness of the attack in comparison to a na\"{i}ve camera blinding attack, showing that common image distortion metrics can not detect the attack presence. Therefore, we present a new, accurate and lightweight enhancement to the backbone network of an object detector to recognize rolling shutter attacks. Overall, our results indicate that rolling shutter attacks can substantially reduce the performance and reliability of vision-based intelligent systems.