  The deep neural network (DNN) models for object detection using camera images
are widely adopted in autonomous vehicles. However, DNN models are shown to be
susceptible to adversarial image perturbations. In the existing methods of
generating the adversarial image perturbations, optimizations take each
incoming image frame as the decision variable to generate an image
perturbation. Therefore, given a new image, the typically
computationally-expensive optimization needs to start over as there is no
learning between the independent optimizations. Very few approaches have been
developed for attacking online image streams while considering the underlying
physical dynamics of autonomous vehicles, their mission, and the environment.
We propose a multi-level stochastic optimization framework that monitors an
attacker's capability of generating the adversarial perturbations. Based on
this capability level, a binary decision attack/not attack is introduced to
enhance the effectiveness of the attacker. We evaluate our proposed multi-level
image attack framework using simulations for vision-guided autonomous vehicles
and actual tests with a small indoor drone in an office environment. The
results show our method's capability to generate the image attack in real-time
while monitoring when the attacker is proficient given state estimates.
