  Interpreting machine learning models is challenging but crucial for ensuring
the safety of deep networks in autonomous driving systems. Due to the
prevalence of deep learning based perception models in autonomous vehicles,
accurately interpreting their predictions is crucial. While a variety of such
methods have been proposed, most are shown to lack robustness. Yet, little has
been done to provide certificates for interpretability robustness. Taking a
step in this direction, we present CORGI, short for Certifiably prOvable
Robustness Guarantees for Interpretability mapping. CORGI is an algorithm that
takes in an input image and gives a certifiable lower bound for the robustness
of the top k pixels of its CAM interpretability map. We show the effectiveness
of CORGI via a case study on traffic sign data, certifying lower bounds on the
minimum adversarial perturbation not far from (4-5x) state-of-the-art attack
methods.
