  Recent neural network architectures have claimed to explain data from the
human visual cortex. Their demonstrated performance is however still limited by
the dependence on exploiting low-level features for solving visual tasks. This
strategy limits their performance in case of out-of-distribution/adversarial
data. Humans, meanwhile learn abstract concepts and are mostly unaffected by
even extreme image distortions. Humans and networks employ strikingly different
strategies to solve visual tasks. To probe this, we introduce a novel set of
image transforms and evaluate humans and networks on an object recognition
task. We found performance for a few common networks quickly decreases while
humans are able to recognize objects with a high accuracy.
