  With the ongoing rise of machine learning, the need for methods for
explaining decisions made by artificial intelligence systems is becoming a more
and more important topic. Especially for image classification tasks, many
state-of-the-art tools to explain such classifiers rely on visual highlighting
of important areas of the input data. Contrary, counterfactual explanation
systems try to enable a counterfactual reasoning by modifying the input image
in a way such that the classifier would have made a different prediction. By
doing so, the users of counterfactual explanation systems are equipped with a
completely different kind of explanatory information. However, methods for
generating realistic counterfactual explanations for image classifiers are
still rare. In this work, we present a novel approach to generate such
counterfactual image explanations based on adversarial image-to-image
translation techniques. Additionally, we conduct a user study to evaluate our
approach in a use case which was inspired by a healthcare scenario. Our results
show that our approach leads to significantly better results regarding mental
models, explanation satisfaction, trust, emotions, and self-efficacy than two
state-of-the art systems that work with saliency maps, namely LIME and LRP.
