  Preprocessing and outlier detection techniques have both been applied to
neural networks to increase robustness with varying degrees of success. In this
paper, we formalize the ideal preprocessor function as one that would take any
input and set it to the nearest in-distribution input. In other words, we
detect any anomalous pixels and set them such that the new input is
in-distribution. We then illustrate a relaxed solution to this problem in the
context of patch attacks. Specifically, we demonstrate that we can model
constraints on the patch attack that specify regions as out of distribution.
With these constraints, we are able to preprocess inputs successfully,
increasing robustness on CARLA object detection.
