  Since the training data of the target model is not available in the black-box
substitute attack, most recent schemes utilize GANs to generate data for
training the substitute model. However, these GANs-based schemes suffer from
low training efficiency as the generator needs to be retrained for each target
model during the substitute training process, as well as low generation
quality. To overcome these limitations, we consider utilizing the diffusion
model to generate data, and propose a novel data-free substitute attack scheme
based on the Stable Diffusion (SD) to improve the efficiency and accuracy of
substitute training. Despite the data generated by the SD exhibiting high
quality, it presents a different distribution of domains and a large variation
of positive and negative samples for the target model. For this problem, we
propose Latent Code Augmentation (LCA) to facilitate SD in generating data that
aligns with the data distribution of the target model. Specifically, we augment
the latent codes of the inferred member data with LCA and use them as guidance
for SD. With the guidance of LCA, the data generated by the SD not only meets
the discriminative criteria of the target model but also exhibits high
diversity. By utilizing this data, it is possible to train the substitute model
that closely resembles the target model more efficiently. Extensive experiments
demonstrate that our LCA achieves higher attack success rates and requires
fewer query budgets compared to GANs-based schemes for different target models.
Our codes are available at \url{https://github.com/LzhMeng/LCA}.
