  Convolutional Neural Networks and Deep Learning classification systems in
general have been shown to be vulnerable to attack by specially crafted data
samples that appear to belong to one class but are instead classified as
another, commonly known as adversarial examples. A variety of attack strategies
have been proposed to craft these samples; however, there is no standard model
that is used to compare the success of each type of attack. Furthermore, there
is no literature currently available that evaluates how common hyperparameters
and optimization strategies may impact a model's ability to resist these
samples. This research bridges that lack of awareness and provides a means for
the selection of training and model parameters in future research on evasion
attacks against convolutional neural networks. The findings of this work
indicate that the selection of model hyperparameters does impact the ability of
a model to resist attack, although they alone cannot prevent the existence of
adversarial examples.
