  State-of-the-art deep neural networks have been shown to be extremely
powerful in a variety of perceptual tasks like semantic segmentation. However,
these networks are vulnerable to adversarial perturbations of the input which
are imperceptible for humans but lead to incorrect predictions. Treating image
segmentation as a sum of pixel-wise classifications, adversarial attacks
developed for classification models were shown to be applicable to segmentation
models as well. In this work, we present simple uncertainty-based weighting
schemes for the loss functions of such attacks that (i) put higher weights on
pixel classifications which can more easily perturbed and (ii) zero-out the
pixel-wise losses corresponding to those pixels that are already confidently
misclassified. The weighting schemes can be easily integrated into the loss
function of a range of well-known adversarial attackers with minimal additional
computational overhead, but lead to significant improved perturbation
performance, as we demonstrate in our empirical analysis on several datasets
and models.
