Transferable Adversarial Examples in AI: Examining transferable adversarial examples and their implications for the robustness of AI systems

Abstract

Adversarial examples are inputs to machine learning models that are intentionally designed to cause the model to make a mistake. Transferable adversarial examples are those that can fool multiple models, even if the models were trained on different datasets or by different organizations. Understanding transferable adversarial examples is crucial for assessing the robustness of AI systems. This paper provides an overview of transferable adversarial examples, discusses their implications for AI systems, and explores current research directions for defending against them.