  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.
