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.

本文研究了扩散模型对于人工智能生成内容，特别是艺术创作的成功，并提出了针对侵权的对抗样本有效性方案。我们探讨了对抗样本的可迁移性问题并利用融合与修改的对抗损失函数明显提高了其可迁移性。实验结果表明我们的方法比现有方法更能生成可迁移且更具有对抗攻击鲁棒性的对抗样本。