Area is all you need: repeatable elements make stronger adversarial
  attacks
Dillon Niederhut
Over the last decade, deep neural networks have achieved state of the art in
computer vision tasks. These models, however, are susceptible to unusual
inputs, known as adversarial examples, that cause them to misclassify or
otherwise fail to detect objects. Here, we provide evidence that the increasing
success of adversarial attacks is primarily due to increasing their size. We
then demonstrate a method for generating the largest possible adversarial patch
by building a adversarial pattern out of repeatable elements. This approach
achieves a new state of the art in evading detection by YOLOv2 and YOLOv3.
Finally, we present an experiment that fails to replicate the prior success of
several attacks published in this field, and end with some comments on testing
and reproducibility.