  State-of-the-art deep classifiers are intriguingly vulnerable to universal
adversarial perturbations: single disturbances of small magnitude that lead to
misclassification of most in-puts. This phenomena may potentially result in a
serious security problem. Despite the extensive research in this area,there is
a lack of theoretical understanding of the structure of these perturbations. In
image domain, there is a certain visual similarity between patterns, that
represent these perturbations, and classical Turing patterns, which appear as a
solution of non-linear partial differential equations and are underlying
concept of many processes in nature. In this paper,we provide a theoretical
bridge between these two different theories, by mapping a simplified algorithm
for crafting universal perturbations to (inhomogeneous) cellular automata,the
latter is known to generate Turing patterns. Furthermore,we propose to use
Turing patterns, generated by cellular automata, as universal perturbations,
and experimentally show that they significantly degrade the performance of deep
learning models. We found this method to be a fast and efficient way to create
a data-agnostic quasi-imperceptible perturbation in the black-box scenario. The
source code is available at https://github.com/NurislamT/advTuring.
