  The classification decisions of neural networks can be misled by small
imperceptible perturbations. This work aims to explain the misled
classifications using saliency methods. The idea behind saliency methods is to
explain the classification decisions of neural networks by creating so-called
saliency maps. Unfortunately, a number of recent publications have shown that
many of the proposed saliency methods do not provide insightful explanations. A
prominent example is Guided Backpropagation (GuidedBP), which simply performs
(partial) image recovery. However, our numerical analysis shows the saliency
maps created by GuidedBP do indeed contain class-discriminative information. We
propose a simple and efficient way to enhance the saliency maps. The proposed
enhanced GuidedBP shows the state-of-the-art performance to explain adversary
classifications.
