Real-Time Detection of Adversarial Attacks in Deep Learning Models

Abstract

This paper explores methods for detecting adversarial examples in real-time systems, with a focus on the challenges and solutions associated with ensuring the robustness of machine learning models in dynamic environments. Adversarial attacks pose significant risks to the integrity and reliability of real-time systems, making effective detection crucial. We review current detection techniques, propose new methodologies, and evaluate their performance in real-time scenarios.