  Deep Neural networks have gained lots of attention in recent years thanks to
the breakthroughs obtained in the field of Computer Vision. However, despite
their popularity, it has been shown that they provide limited robustness in
their predictions. In particular, it is possible to synthesise small
adversarial perturbations that imperceptibly modify a correctly classified
input data, making the network confidently misclassify it. This has led to a
plethora of different methods to try to improve robustness or detect the
presence of these perturbations. In this paper, we perform an analysis of
$\beta$-Variational Classifiers, a particular class of methods that not only
solve a specific classification task, but also provide a generative component
that is able to generate new samples from the input distribution. More in
details, we study their robustness and detection capabilities, together with
some novel insights on the generative part of the model.
