  Deep neural networks and in particular, deep neural classifiers have become
an integral part of many modern applications. Despite their practical success,
we still have limited knowledge of how they work and the demand for such an
understanding is evergrowing. In this regard, one crucial aspect of deep neural
network classifiers that can help us deepen our knowledge about their
decision-making behavior is to investigate their decision boundaries.
Nevertheless, this is contingent upon having access to samples populating the
areas near the decision boundary. To achieve this, we propose a novel approach
we call Deep Decision boundary Instance Generation (DeepDIG). DeepDIG utilizes
a method based on adversarial example generation as an effective way of
generating samples near the decision boundary of any deep neural network model.
Then, we introduce a set of important principled characteristics that take
advantage of the generated instances near the decision boundary to provide
multifaceted understandings of deep neural networks. We have performed
extensive experiments on multiple representative datasets across various deep
neural network models and characterized their decision boundaries. The code is
publicly available at https://github.com/hamidkarimi/DeepDIG/.
