  Since the discovery of adversarial attacks against machine learning models
nearly a decade ago, research on adversarial machine learning has rapidly
evolved into an eternal war between defenders, who seek to increase the
robustness of ML models against adversarial attacks, and adversaries, who seek
to develop better attacks capable of weakening or defeating these defenses.
This domain, however, has found little buy-in from ML practitioners, who are
neither overtly concerned about these attacks affecting their systems in the
real world nor are willing to trade off the accuracy of their models in pursuit
of robustness against these attacks.
  In this paper, we motivate the design and implementation of Ares, an
evaluation framework for adversarial ML that allows researchers to explore
attacks and defenses in a realistic wargame-like environment. Ares frames the
conflict between the attacker and defender as two agents in a reinforcement
learning environment with opposing objectives. This allows the introduction of
system-level evaluation metrics such as time to failure and evaluation of
complex strategies such as moving target defenses. We provide the results of
our initial exploration involving a white-box attacker against an adversarially
trained defender.

Machine Learning;Artificial Intelligence;Cryptography and Security