  In the last years, AI systems, in particular neural networks, have seen a
tremendous increase in performance, and they are now used in a broad range of
applications. Unlike classical symbolic AI systems, neural networks are trained
using large data sets and their inner structure containing possibly billions of
parameters does not lend itself to human interpretation. As a consequence, it
is so far not feasible to provide broad guarantees for the correct behaviour of
neural networks during operation if they process input data that significantly
differ from those seen during training. However, many applications of AI
systems are security- or safety-critical, and hence require obtaining
statements on the robustness of the systems when facing unexpected events,
whether they occur naturally or are induced by an attacker in a targeted way.
As a step towards developing robust AI systems for such applications, this
paper presents how the robustness of AI systems can be practically examined and
which methods and metrics can be used to do so. The robustness testing
methodology is described and analysed for the example use case of traffic sign
recognition in autonomous driving.
