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Recent advances in adversarial machine learning have shown that defensesconsidered to be robust are actually susceptible to adversarial attacks whichare specifically customized to target their weaknesses. These defenses includeBarrage of Random Transforms (BaRT), Friendly Adversarial Training (FAT), Trashis Treasure (TiT) and ensemble models made up of Vision Transformers (ViTs),Big Transfer models and Spiking Neural Networks (SNNs). We first conduct atransferability analysis, to demonstrate the adversarial examples generated bycustomized attacks on one defense, are not often misclassified by anotherdefense.  This finding leads to two important questions. First, how can the lowtransferability between defenses be utilized in a game theoretic framework toimprove the robustness? Second, how can an adversary within this frameworkdevelop effective multi-model attacks? In this paper, we provide agame-theoretic framework for ensemble adversarial attacks and defenses. Ourframework is called Game theoretic Mixed Experts (GaME). It is designed to findthe Mixed-Nash strategy for both a detector based and standard defender, whenfacing an attacker employing compositional adversarial attacks. We furtherpropose three new attack algorithms, specifically designed to target defenseswith randomized transformations, multi-model voting schemes, and adversarialdetector architectures. These attacks serve to both strengthen defensesgenerated by the GaME framework and verify their robustness against unforeseenattacks. Overall, our framework and analyses advance the field of adversarialmachine learning by yielding new insights into compositional attack and defenseformulations.

Game Theoretic Mixed Experts for Combinational Adversarial Machine Learning