Mist: Towards Improved Adversarial Examples for Diffusion Models
Chumeng Liang Xiaoyu Wu
Diffusion Models (DMs) have empowered great success in
artificial-intelligence-generated content, especially in artwork creation, yet
raising new concerns in intellectual properties and copyright. For example,
infringers can make profits by imitating non-authorized human-created paintings
with DMs. Recent researches suggest that various adversarial examples for
diffusion models can be effective tools against these copyright infringements.
However, current adversarial examples show weakness in transferability over
different painting-imitating methods and robustness under straightforward
adversarial defense, for example, noise purification. We surprisingly find that
the transferability of adversarial examples can be significantly enhanced by
exploiting a fused and modified adversarial loss term under consistent
parameters. In this work, we comprehensively evaluate the cross-method
transferability of adversarial examples. The experimental observation shows
that our method generates more transferable adversarial examples with even
stronger robustness against the simple adversarial defense.