Nina Shamsi (Northeastern University), Kaeshav Chandrasekar, Yan Long, Christopher Limbach (University of Michigan), Keith Rebello (Boeing), Kevin Fu (Northeastern University)

Control or disablement of computer vision-assisted autonomous vehicles via acoustic interference is an open problem in vehicle cybersecurity research. This work explores a new threat model in this problem space: acoustic interference via high-speed, pulsed lasers to non-destructively affect drone sensors. Initial experiments verified the feasibility of laser-induced acoustic wave generation at resonant frequencies of MEMS gyroscope sensors. Acoustic waves generated by a lab-scale laser produced a 300-fold noise floor modification in commercial off-of-the-shelf (COTS) gyroscope sensor readings. Computer vision functionalities of drones often depend on such vulnerable sensors, and can be a target of this new threat model because of camera motion blurs caused by acoustic interference. The effect of laser-induced acoustics in object detection datasets was simulated by extracting blur kernels from drone images captured under different conditions of acoustic interference, including speaker-generated sound to emulate higher intensity lasers, and evaluated using state-of-theart object detection models. The results show an average of 41.1% decrease in mean average precision for YOLOv8 across two datasets, and suggest an inverse relationship between an object detection model’s mean average precision and acoustic intensity. Object detection models with at least 60M parameters appear more resilient against laser-induced acoustic interference. Initial characterizations of laser-induced acoustic interference reveal future potential threat models affecting sensors and downstream software systems of autonomous vehicles.

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