  Adversarial examples, or nearly indistinguishable inputs created by an
attacker, significantly reduce machine learning accuracy. Theoretical evidence
has shown that the high intrinsic dimensionality of datasets facilitates an
adversary's ability to develop effective adversarial examples in classification
models. Adjacently, the presentation of data to a learning model impacts its
performance. For example, we have seen this through dimensionality reduction
techniques used to aid with the generalization of features in machine learning
applications. Thus, data transformation techniques go hand-in-hand with
state-of-the-art learning models in decision-making applications such as
intelligent medical or military systems. With this work, we explore how data
transformations techniques such as feature selection, dimensionality reduction,
or trend extraction techniques may impact an adversary's ability to create
effective adversarial samples on a recurrent neural network. Specifically, we
analyze it from the perspective of the data manifold and the presentation of
its intrinsic features. Our evaluation empirically shows that feature selection
and trend extraction techniques may increase the RNN's vulnerability. A data
transformation technique reduces the vulnerability to adversarial examples only
if it approximates the dataset's intrinsic dimension, minimizes codimension,
and maintains higher manifold coverage.
