  With the perpetual increase of complexity of the state-of-the-art deep neural
networks, it becomes a more and more challenging task to maintain their
interpretability. Our work aims to evaluate the effects of adversarial training
utilized to produce robust models - less vulnerable to adversarial attacks. It
has been shown to make computer vision models more interpretable.
Interpretability is as essential as robustness when we deploy the models to the
real world. To prove the correlation between these two problems, we extensively
examine the models using local feature-importance methods (SHAP, Integrated
Gradients) and feature visualization techniques (Representation Inversion,
Class Specific Image Generation). Standard models, compared to robust are more
susceptible to adversarial attacks, and their learned representations are less
meaningful to humans. Conversely, these models focus on distinctive regions of
the images which support their predictions. Moreover, the features learned by
the robust model are closer to the real ones.
