  The task of detecting morphed face images has become highly relevant in
recent years to ensure the security of automatic verification systems based on
facial images, e.g. automated border control gates. Detection methods based on
Deep Neural Networks (DNN) have been shown to be very suitable to this end.
However, they do not provide transparency in the decision making and it is not
clear how they distinguish between genuine and morphed face images. This is
particularly relevant for systems intended to assist a human operator, who
should be able to understand the reasoning. In this paper, we tackle this
problem and present Focused Layer-wise Relevance Propagation (FLRP). This
framework explains to a human inspector on a precise pixel level, which image
regions are used by a Deep Neural Network to distinguish between a genuine and
a morphed face image. Additionally, we propose another framework to objectively
analyze the quality of our method and compare FLRP to other DNN
interpretability methods. This evaluation framework is based on removing
detected artifacts and analyzing the influence of these changes on the decision
of the DNN. Especially, if the DNN is uncertain in its decision or even
incorrect, FLRP performs much better in highlighting visible artifacts compared
to other methods.
