Attacker Model

Attacker models in machine learning research aim to simulate adversarial strategies to evaluate the robustness of machine learning systems. Current research focuses on characterizing attacker capabilities in various scenarios, including limited-query attacks, federated learning settings, and the transferability of adversarial examples across different model architectures and datasets. Understanding these models is crucial for developing effective defenses against malicious attacks, improving the security and reliability of machine learning systems deployed in critical applications like healthcare and finance. This research directly impacts the development of more robust and secure AI systems.