Efficient Data-free Substitute Attacks Using Latent Code Augmentation with Stable Diffusion

The core message of this paper is to propose a novel Latent Code Augmentation (LCA) method that leverages the pre-trained Stable Diffusion model to efficiently generate data for training a substitute model that closely resembles the target model, thereby enabling effective black-box attacks without access to the target model's training data.