  Given the outstanding progress that convolutional neural networks (CNNs) have
made on natural image classification and object recognition problems, it is
shown that deep learning methods can achieve very good recognition performance
on many texture datasets. However, while CNNs for natural image
classification/object recognition tasks have been revealed to be highly
vulnerable to various types of adversarial attack methods, the robustness of
deep learning methods for texture recognition is yet to be examined. In our
paper, we show that there exist small image-agnostic/univesal perturbations
that can fool the deep learning models with more than 80\% of testing fooling
rates on all tested texture datasets. The computed perturbations using various
attack methods on the tested datasets are generally quasi-imperceptible,
containing structured patterns with low, middle and high frequency components.
