  Deep neural networks (DNNs) are shown to be susceptible to adversarial
example attacks. Most existing works achieve this malicious objective by
crafting subtle pixel-wise perturbations, and they are difficult to launch in
the physical world due to inevitable transformations (e.g., different
photographic distances and angles). Recently, there are a few research works on
generating physical adversarial examples, but they generally require the
details of the model a priori, which is often impractical. In this work, we
propose a novel physical adversarial attack for arbitrary black-box DNN models,
namely Region-Wise Attack. To be specific, we present how to efficiently search
for regionwise perturbations to the inputs and determine their shapes,
locations and colors via both top-down and bottom-up techniques. In addition,
we introduce two fine-tuning techniques to further improve the robustness of
our attack. Experimental results demonstrate the efficacy and robustness of the
proposed Region-Wise Attack in real world.
