Shadow Model

Shadow models are used in various machine learning contexts, primarily to assess model vulnerabilities and improve performance. Current research focuses on applying shadow models to membership inference attacks (determining if data was used in training), improving the efficiency of these attacks, and developing shadow-based defenses against backdoor attacks in federated learning. These techniques leverage diverse architectures, including quantile regression models and neural networks, and have significant implications for data privacy and the security of large language models and other AI systems. Furthermore, shadow models are being explored in computer vision for tasks like image relighting and shadow generation/removal, enhancing realism in 3D rendering and improving image quality.