  There is considerable evidence that deep neural networks are vulnerable to
adversarial perturbations applied directly to their digital inputs. However, it
remains an open question whether this translates to vulnerabilities in real
systems. For example, an attack on self-driving cars would in practice entail
modifying the driving environment, which then impacts the video inputs to the
car's controller, thereby indirectly leading to incorrect driving decisions.
Such attacks require accounting for system dynamics and tracking viewpoint
changes. We propose a scalable approach for finding adversarial modifications
of a simulated autonomous driving environment using a differentiable
approximation for the mapping from environmental modifications (rectangles on
the road) to the corresponding video inputs to the controller neural network.
Given the parameters of the rectangles, our proposed differentiable mapping
composites them onto pre-recorded video streams of the original environment,
accounting for geometric and color variations. Moreover, we propose a multiple
trajectory sampling approach that enables our attacks to be robust to a car's
self-correcting behavior. When combined with a neural network-based controller,
our approach allows the design of adversarial modifications through end-to-end
gradient-based optimization. Using the Carla autonomous driving simulator, we
show that our approach is significantly more scalable and far more effective at
identifying autonomous vehicle vulnerabilities in simulation experiments than a
state-of-the-art approach based on Bayesian Optimization.
