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Title : Security Vulnerability of Rolling Shutter CMOS Sensors
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Security Vulnerability of Rolling Shutter CMOS Sensors

Arxiv.org paper "They See Me Rollin': Inherent Vulnerability of the Rolling Shutter in CMOS Image Sensors" by Sebastian Köhler, Giulio Lovisotto, Simon Birnbach, Richard Baker, and Ivan Martinovic from Oxford University, UK, warns of security problem in machine vision systems relying on rolling shutter sensors.

"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."

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