  The growing incorporation of artificial neural networks (NNs) into many
fields, and especially into life-critical systems, is restrained by their
vulnerability to adversarial examples (AEs). Some existing defense methods can
increase NNs' robustness, but they often require special architecture or
training procedures and are irrelevant to already trained models. In this
paper, we propose a simple defense that combines feature visualization with
input modification, and can, therefore, be applicable to various pre-trained
networks. By reviewing several interpretability methods, we gain new insights
regarding the influence of AEs on NNs' computation. Based on that, we
hypothesize that information about the "true" object is preserved within the
NN's activity, even when the input is adversarial, and present a feature
visualization version that can extract that information in the form of
relevance heatmaps. We then use these heatmaps as a basis for our defense, in
which the adversarial effects are corrupted by massive blurring. We also
provide a new evaluation metric that can capture the effects of both attacks
and defenses more thoroughly and descriptively, and demonstrate the
effectiveness of the defense and the utility of the suggested evaluation
measurement with VGG19 results on the ImageNet dataset.
