Rolling in the Deep: Exploiting Rolling Shutter Effect Against Stereo Depth Estimation in Drones
Dongfang Guo, Rui Tan · 2025
Stereo vision plays a critical role in enabling depth perception for drones, supporting navigation and obstacle avoidance in complex environments. However, the robustness and security of stereo vision systems remain largely underexplored. In this paper, we propose Rolling in the Deep (RiD), a novel physical attack that exploits the rolling shutter effect (RSE) to inject imperceptible, structured perturbations into stereo image pairs. We analyze RSE formation in binocular camera setups and show how RSE-based perturbations can degrade deep learning-based stereo matching by exploiting model vulnerabilities and sensor misalignments, resulting in incorrect depth estimation. Preliminary results show the feasibility of RiD under realistic stereo configurations, revealing a new class of threats to drone perception systems.