Abstract
Adversarial machine learning, which aims at tricking ML models by providing deceptive inputs, has been identified as a powerful method to improve various trustworthiness metrics (e.g., adversarial robustness, explainability, and fairness) and to advance versatile ML paradigms (e.g., supervised and self-supervised learning, and static and continual learning). As a consequence of the proliferation of AdvML-inspired research works, the proposed workshop – New Frontiers in AdvML – aims to identify the challenges and limitations of current AdvML methods, and explore new perspectives and constructive views of AdvML across the full theory/algorithm/application stack. See https://advml-frontier.github.io/ for more details.