  Due to their complex nature, it is hard to characterize the ways in which
machine learning models can misbehave or be exploited when deployed. Recent
work on adversarial examples, i.e. inputs with minor perturbations that result
in substantially different model predictions, is helpful in evaluating the
robustness of these models by exposing the adversarial scenarios where they
fail. However, these malicious perturbations are often unnatural, not
semantically meaningful, and not applicable to complicated domains such as
language. In this paper, we propose a framework to generate natural and legible
adversarial examples that lie on the data manifold, by searching in semantic
space of dense and continuous data representation, utilizing the recent
advances in generative adversarial networks. We present generated adversaries
to demonstrate the potential of the proposed approach for black-box classifiers
for a wide range of applications such as image classification, textual
entailment, and machine translation. We include experiments to show that the
generated adversaries are natural, legible to humans, and useful in evaluating
and analyzing black-box classifiers.
