  We are concerned with the vulnerability of computer vision models to
distributional shifts. We formulate a combinatorial optimization problem that
allows evaluating the regions in the image space where a given model is more
vulnerable, in terms of image transformations applied to the input, and face it
with standard search algorithms. We further embed this idea in a training
procedure, where we define new data augmentation rules according to the image
transformations that the current model is most vulnerable to, over iterations.
An empirical evaluation on classification and semantic segmentation problems
suggests that the devised algorithm allows to train models that are more robust
against content-preserving image manipulations and, in general, against
distributional shifts.
