State of the Art Defense

State-of-the-art defense in machine learning focuses on enhancing the robustness and security of models against various adversarial attacks, including backdoors, poisoning, and model stealing. Current research emphasizes developing lightweight and efficient defense mechanisms, often employing techniques like data purification, layered aggregation, generative models, and anomaly detection across diverse model architectures (e.g., deep neural networks, federated learning models, and large language models). These advancements are crucial for ensuring the reliability and trustworthiness of AI systems in critical applications, ranging from autonomous vehicles to cybersecurity and healthcare. The field is actively exploring both theoretical guarantees and empirical evaluations to improve the effectiveness and practicality of defenses.