  Adversarial examples are some special input that can perturb the output of a
deep neural network, in order to make produce intentional errors in the
learning algorithms in the production environment. Most of the present methods
for generating adversarial examples require gradient information. Even
universal perturbations that are not relevant to the generative model rely to
some extent on gradient information. Procedural noise adversarial examples is a
new way of adversarial example generation, which uses computer graphics noise
to generate universal adversarial perturbations quickly while not relying on
gradient information. Combined with the defensive idea of adversarial training,
we use Perlin noise to train the neural network to obtain a model that can
defend against procedural noise adversarial examples. In combination with the
use of model fine-tuning methods based on pre-trained models, we obtain faster
training as well as higher accuracy. Our study shows that procedural noise
adversarial examples are defensible, but why procedural noise can generate
adversarial examples and how to defend against other kinds of procedural noise
adversarial examples that may emerge in the future remain to be investigated.
