  Deep Neural Networks (DNNs) are known to be susceptible to adversarial
examples. Adversarial examples are maliciously crafted inputs that are designed
to fool a model, but appear normal to human beings. Recent work has shown that
pixel discretization can be used to make classifiers for MNIST highly robust to
adversarial examples. However, pixel discretization fails to provide
significant protection on more complex datasets. In this paper, we take the
first step towards reconciling these contrary findings. Focusing on the
observation that discrete pixelization in MNIST makes the background completely
black and foreground completely white, we hypothesize that the important
property for increasing robustness is the elimination of image background using
attention masks before classifying an object. To examine this hypothesis, we
create foreground attention masks for two different datasets, GTSRB and
MS-COCO. Our initial results suggest that using attention mask leads to
improved robustness. On the adversarially trained classifiers, we see an
adversarial robustness increase of over 20% on MS-COCO.
