  Cameras have become a fundamental component of vision-based intelligent
systems. As a balance between production costs and image quality, most modern
cameras use Complementary Metal-Oxide Semiconductor image sensors that
implement an electronic rolling shutter mechanism, where image rows are
captured consecutively rather than all-at-once.
  In this paper, we describe how the electronic rolling shutter can be
exploited using a bright, modulated light source (e.g., an inexpensive,
off-the-shelf laser), to inject fine-grained image disruptions. These
disruptions substantially affect camera-based computer vision systems, where
high-frequency data is crucial in extracting informative features from objects.
  We study the fundamental factors affecting a rolling shutter attack, such as
environmental conditions, angle of the incident light, laser to camera
distance, and aiming precision. We demonstrate how these factors affect the
intensity of the injected distortion and how an adversary can take them into
account by modeling the properties of the camera. We introduce a general
pipeline of a practical attack, which consists of: (i) profiling several
properties of the target camera and (ii) partially simulating the attack to
find distortions that satisfy the adversary's goal. Then, we instantiate the
attack to the scenario of object detection, where the adversary's goal is to
maximally disrupt the detection of objects in the image. We show that the
adversary can modulate the laser to hide up to 75% of objects perceived by
state-of-the-art detectors while controlling the amount of perturbation to keep
the attack inconspicuous. Our results indicate that rolling shutter attacks can
substantially reduce the performance and reliability of vision-based
intelligent systems.
