Black Box Model Inversion Attack

Black box model inversion attacks aim to reconstruct the private training data used to create a machine learning model, solely by querying the model's outputs without access to its internal workings. Current research focuses on improving attack efficiency and accuracy using techniques like generative adversarial networks (GANs), reinforcement learning, and novel latent space manipulation methods to overcome limitations of previous approaches. These attacks highlight significant vulnerabilities in data privacy within machine learning systems, prompting ongoing research into more robust privacy-preserving techniques and raising concerns about the responsible deployment of AI models.