  Autonomous systems increasingly rely on machine learning techniques to
transform high-dimensional raw inputs into predictions that are then used for
decision-making and control. However, it is often easy to maliciously
manipulate such inputs and, as a result, predictions. While effective
techniques have been proposed to certify the robustness of predictions to
adversarial input perturbations, such techniques have been disembodied from
control systems that make downstream use of the predictions. We propose the
first approach for composing robustness certification of predictions with
respect to raw input perturbations with robust control to obtain certified
robustness of control to adversarial input perturbations. We use a case study
of adaptive vehicle control to illustrate our approach and show the value of
the resulting end-to-end certificates through extensive experiments.
