Latent diffusion models achieve state-of-the-art performance on a variety of
generative tasks, such as image synthesis and image editing. However, the
robustness of latent diffusion models is not well studied. Previous works only
focus on the adversarial attacks against the encoder or the output image under
white-box settings, regardless of the denoising process. Therefore, in this
paper, we aim to analyze the robustness of latent diffusion models more
thoroughly. We first study the influence of the components inside latent
diffusion models on their white-box robustness. In addition to white-box
scenarios, we evaluate the black-box robustness of latent diffusion models via
transfer attacks, where we consider both prompt-transfer and model-transfer
settings and possible defense mechanisms. However, all these explorations need
a comprehensive benchmark dataset, which is missing in the literature.
Therefore, to facilitate the research of the robustness of latent diffusion
models, we propose two automatic dataset construction pipelines for two kinds
of image editing models and release the whole dataset. Our code and dataset are
available at https://github.com/jpzhang1810/LDM-Robustness.

该研究通过分析内部组件对白盒健壮性的影响并评估黑盒健壮性，研究了潜在扩散模型的鲁棒性，发现文献中缺少完整的基准数据集，缺陷自动数据集构建管道可提供数据，用以发掘该模型鲁棒性。