Prior Defense

Prior defense in machine learning focuses on protecting models from various attacks, including membership inference, model stealing, and backdoor attacks, aiming to improve the privacy and security of AI systems. Current research emphasizes developing robust and efficient defense mechanisms, often leveraging generative models or gradient manipulation techniques, and critically evaluating the effectiveness of these defenses through rigorous testing methodologies. This field is crucial for building trustworthy AI systems, as it directly addresses vulnerabilities that could lead to data breaches, model compromise, and biased or manipulated outputs, impacting both the reliability of AI and its societal impact.