  Neural Networks have been shown to be sensitive to common perturbations such
as blur, Gaussian noise, rotations, etc. They are also vulnerable to some
artificial malicious corruptions called adversarial examples. The adversarial
examples study has recently become very popular and it sometimes even reduces
the term "adversarial robustness" to the term "robustness". Yet, we do not know
to what extent the adversarial robustness is related to the global robustness.
Similarly, we do not know if a robustness to various common perturbations such
as translations or contrast losses for instance, could help with adversarial
corruptions. We intend to study the links between the robustnesses of neural
networks to both perturbations. With our experiments, we provide one of the
first benchmark designed to estimate the robustness of neural networks to
common perturbations. We show that increasing the robustness to carefully
selected common perturbations, can make neural networks more robust to unseen
common perturbations. We also prove that adversarial robustness and robustness
to common perturbations are independent. Our results make us believe that
neural network robustness should be addressed in a broader sense.
