Model Extraction and Defenses on Generative Adversarial Networks

Understand

Model extraction attacks aim to duplicate a machine learning model through query access to a target model.

• Early studies mainly focus on discriminative models.
• Despite the success, model extraction attacks against generative models are less well explored.
• In this paper, we systematically study the feasibility of model extraction attacks against generative adversarial networks (GANs).