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In this paper, we describe how the electronic rolling shutter in CMOS imagesensors can be exploited using a bright, modulated light source (e.g., aninexpensive, off-the-shelf laser), to inject fine-grained image disruptions. Wedemonstrate the attack on seven different CMOS cameras, ranging from cheap IoTto semi-professional surveillance cameras, to highlight the wide applicabilityof the rolling shutter attack. We model the fundamental factors affecting arolling shutter attack in an uncontrolled setting. We then perform anexhaustive evaluation of the attack's effect on the task of object detection,investigating the effect of attack parameters. We validate our model againstempirical data collected on two separate cameras, showing that by simply usinginformation from the camera's datasheet the adversary can accurately predictthe injected distortion size and optimize their attack accordingly. We findthat an adversary can hide up to 75% of objects perceived by state-of-the-artdetectors by selecting appropriate attack parameters. We also investigate thestealthiness of the attack in comparison to a na\"{i}ve camera blinding attack,showing that common image distortion metrics can not detect the attackpresence. Therefore, we present a new, accurate and lightweight enhancement tothe backbone network of an object detector to recognize rolling shutterattacks. Overall, our results indicate that rolling shutter attacks cansubstantially reduce the performance and reliability of vision-basedintelligent systems.

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