Model Inversion

Model inversion (MI) is a technique used to reconstruct training data from a machine learning model's output, raising significant privacy concerns. Current research focuses on developing more effective MI attacks using generative adversarial networks (GANs) and other deep learning architectures, as well as designing robust defenses, such as data augmentation methods and transfer learning techniques, to mitigate these privacy risks. The ongoing development and refinement of MI attacks and defenses are crucial for ensuring the responsible development and deployment of machine learning models, particularly in sensitive applications.