Florian Merkle

When Should You Defend Your Classifier -- A Game-theoretical Analysis of Countermeasures against Adversarial Examples

2021 · arXiv (Cornell University)

Adversarial machine learning, i.e., increasing the robustness of machine learning algorithms against so-called adversarial examples, is now an established field. Yet, newly proposed methods are evaluated and compared under unrealistic scenarios where costs for adversary …