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Found 4 papers, 1 papers with code
Learning When to Use Adaptive Adversarial Image Perturbations against Autonomous Vehicles
We propose a multi-level stochastic optimization framework that monitors an attacker's capability of generating the adversarial perturbations.
Detection of Stealthy Adversaries for Networked Unmanned Aerial Vehicles*
A network of unmanned aerial vehicles (UAVs) provides distributed coverage, reconfigurability, and maneuverability in performing complex cooperative tasks.
Privacy-Preserving Stealthy Attack Detection in Multi-Agent Control Systems
The detection structure consists of a global (central) observer and local observers for the multi-agent system partitioned into clusters.
Learning Image Attacks toward Vision Guided Autonomous Vehicles
While adversarial neural networks have been shown successful for static image attacks, very few approaches have been developed for attacking online image streams while taking into account the underlying physical dynamics of autonomous vehicles, their mission, and environment.
