  Safety is one of the most important development goals for highly automated
driving (HAD) systems. This applies in particular to the perception function
driven by Deep Neural Networks (DNNs). For these, large parts of the
traditional safety processes and requirements are not fully applicable or
sufficient. The aim of this paper is to present a framework for the description
and mitigation of DNN insufficiencies and the derivation of relevant safety
mechanisms to increase the safety of DNNs. To assess the effectiveness of these
safety mechanisms, we present a categorization scheme for evaluation metrics.
