Title: Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors,
Abstract: We study the problem of generating adversarial examples in a black-box
setting in which only loss-oracle access to a model is available. We introduce
a framework that conceptually unifies much of the existing work on black-box
attacks, and we demonstrate that the current state-of-the-art methods are
optimal in a natural sense. Despite this optimality, we show how to improve
black-box attacks by bringing a new element into the problem: gradient priors.
We give a bandit optimization-based algorithm that allows us to seamlessly
integrate any such priors, and we explicitly identify and incorporate two
examples. The resulting methods use two to four times fewer queries and fail
two to five times less often than the current state-of-the-art.