Pascal Schöttle

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 …

HE-MAN – Homomorphically Encrypted MAchine learning with oNnx models

2023

Machine learning (ML) algorithms are increasingly important for the success of products and services, especially considering the growing amount and availability of data. This also holds for areas handling sensitive data, e.g. applications processing medical …