Recently, many studies utilized adversarial examples (AEs) to raise the cost
of malicious image editing and copyright violation powered by latent diffusion
models (LDMs). Despite their successes, a few have studied the surrogate model
they used to generate AEs. In this paper, from the perspective of adversarial
transferability, we investigate how the surrogate model's property influences
the performance of AEs for LDMs. Specifically, we view the time-step sampling
in the Monte-Carlo-based (MC-based) adversarial attack as selecting surrogate
models. We find that the smoothness of surrogate models at different time steps
differs, and we substantially improve the performance of the MC-based AEs by
selecting smoother surrogate models. In the light of the theoretical framework
on adversarial transferability in image classification, we also conduct a
theoretical analysis to explain why smooth surrogate models can also boost AEs
for LDMs.

通过对代理模型属性的调查，从对抗迁移的角度出发，我们发现选择更平滑的代理模型能够显著提高基于蒙特卡罗的对抗样本的性能，同时通过理论分析解释了为什么平滑的代理模型也可以增强潜在扩散模型的对抗样本。