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Diffusion Models (DMs) have empowered great success inartificial-intelligence-generated content, especially in artwork creation, yetraising new concerns in intellectual properties and copyright. For example,infringers can make profits by imitating non-authorized human-created paintingswith DMs. Recent researches suggest that various adversarial examples fordiffusion models can be effective tools against these copyright infringements.However, current adversarial examples show weakness in transferability overdifferent painting-imitating methods and robustness under straightforwardadversarial defense, for example, noise purification. We surprisingly find thatthe transferability of adversarial examples can be significantly enhanced byexploiting a fused and modified adversarial loss term under consistentparameters. In this work, we comprehensively evaluate the cross-methodtransferability of adversarial examples. The experimental observation showsthat our method generates more transferable adversarial examples with evenstronger robustness against the simple adversarial defense.

Mist: Towards Improved Adversarial Examples for Diffusion Models