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Preprocessing and outlier detection techniques have both been applied toneural networks to increase robustness with varying degrees of success. In thispaper, we formalize the ideal preprocessor function as one that would take anyinput and set it to the nearest in-distribution input. In other words, wedetect any anomalous pixels and set them such that the new input isin-distribution. We then illustrate a relaxed solution to this problem in thecontext of patch attacks. Specifically, we demonstrate that we can modelconstraints 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.

Constraining the Attack Space of Machine Learning Models with Distribution Clamping Preprocessing