  Input perturbation methods occlude parts of an input to a function and
measure the change in the function's output. Recently, input perturbation
methods have been applied to generate and evaluate saliency maps from
convolutional neural networks. In practice, neutral baseline images are used
for the occlusion, such that the baseline image's impact on the classification
probability is minimal. However, in this paper we show that arguably neutral
baseline images still impact the generated saliency maps and their evaluation
with input perturbations. We also demonstrate that many choices of
hyperparameters lead to the divergence of saliency maps generated by input
perturbations. We experimentally reveal inconsistencies among a selection of
input perturbation methods and find that they lack robustness for generating
saliency maps and for evaluating saliency maps as saliency metrics.
