  The brittleness of deep image classifiers to small adversarial input
perturbations has been extensively studied in the last several years. However,
the main objective of existing perturbations is primarily limited to change the
correctly predicted Top-1 class by an incorrect one, which does not intend to
change the Top-k prediction. In many digital real-world scenarios Top-k
prediction is more relevant. In this work, we propose a fast and accurate
method of computing Top-k adversarial examples as a simple multi-objective
optimization. We demonstrate its efficacy and performance by comparing it to
other adversarial example crafting techniques. Moreover, based on this method,
we propose Top-k Universal Adversarial Perturbations, image-agnostic tiny
perturbations that cause the true class to be absent among the Top-k prediction
for the majority of natural images. We experimentally show that our approach
outperforms baseline methods and even improves existing techniques of finding
Universal Adversarial Perturbations.
