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Since the discovery of adversarial attacks against machine learning modelsnearly a decade ago, research on adversarial machine learning has rapidlyevolved into an eternal war between defenders, who seek to increase therobustness of ML models against adversarial attacks, and adversaries, who seekto develop better attacks capable of weakening or defeating these defenses.This domain, however, has found little buy-in from ML practitioners, who areneither overtly concerned about these attacks affecting their systems in thereal world nor are willing to trade off the accuracy of their models in pursuitof robustness against these attacks.  In this paper, we motivate the design and implementation of Ares, anevaluation framework for adversarial ML that allows researchers to exploreattacks and defenses in a realistic wargame-like environment. Ares frames theconflict between the attacker and defender as two agents in a reinforcementlearning environment with opposing objectives. This allows the introduction ofsystem-level evaluation metrics such as time to failure and evaluation ofcomplex strategies such as moving target defenses. We provide the results ofour initial exploration involving a white-box attacker against an adversariallytrained defender.

Ares: A System-Oriented Wargame Framework for Adversarial ML