  We study the problem of defending deep neural network approaches for image
classification from physically realizable attacks. First, we demonstrate that
the two most scalable and effective methods for learning robust models,
adversarial training with PGD attacks and randomized smoothing, exhibit very
limited effectiveness against three of the highest profile physical attacks.
Next, we propose a new abstract adversarial model, rectangular occlusion
attacks, in which an adversary places a small adversarially crafted rectangle
in an image, and develop two approaches for efficiently computing the resulting
adversarial examples. Finally, we demonstrate that adversarial training using
our new attack yields image classification models that exhibit high robustness
against the physically realizable attacks we study, offering the first
effective generic defense against such attacks.
