  Deep neural networks are highly vulnerable to adversarial examples, which
imposes severe security issues for these state-of-the-art models. Many defense
methods have been proposed to mitigate this problem. However, a lot of them
depend on modification or additional training of the target model. In this
work, we analytically investigate each layer's representation of non-perturbed
and perturbed images and show the effect of perturbations on each of these
representations. Accordingly, a method based on whitening coloring transform is
proposed in order to diminish the misrepresentation of any desirable layer
caused by adversaries. Our method can be applied to any layer of any arbitrary
model without the need of any modification or additional training. Due to the
fact that the full whitening of the layer's representation is not easily
differentiable, our proposed method is superbly robust against white-box
attacks. Furthermore, we demonstrate the strength of our method against some
state-of-the-art black-box attacks.
