  To verify and validate networks, it is essential to gain insight into their
decisions, limitations as well as possible shortcomings of training data. In
this work, we propose a post-hoc, optimization based visual explanation method,
which highlights the evidence in the input image for a specific prediction. Our
approach is based on a novel technique to defend against adversarial evidence
(i.e. faulty evidence due to artefacts) by filtering gradients during
optimization. The defense does not depend on human-tuned parameters. It enables
explanations which are both fine-grained and preserve the characteristics of
images, such as edges and colors. The explanations are interpretable, suited
for visualizing detailed evidence and can be tested as they are valid model
inputs. We qualitatively and quantitatively evaluate our approach on a
multitude of models and datasets.
