  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. This paper presents an
online adversarial machine learning framework that can effectively misguide
autonomous vehicles' missions. In the existing image attack methods devised
toward autonomous vehicles, optimization steps are repeated for every image
frame. This framework removes the need for fully converged optimization at
every frame to realize image attacks in real-time. Using reinforcement
learning, a generative neural network is trained over a set of image frames to
obtain an attack policy that is more robust to dynamic and uncertain
environments. A state estimator is introduced for processing image streams to
reduce the attack policy's sensitivity to physical variables such as unknown
position and velocity. A simulation study is provided to validate the results.
