  Deep Neural Networks (DNNs) have often supplied state-of-the-art results in
pattern recognition tasks. Despite their advances, however, the existence of
adversarial examples have caught the attention of the community. Many existing
works have proposed methods for searching for adversarial examples within
fixed-sized regions around training points. Our work complements and improves
these existing approaches by adapting the size of these regions based on the
problem complexity and data sampling density. This makes such approaches more
appropriate for other types of data and may further improve adversarial
training methods by increasing the region sizes without creating incorrect
labels.
