Generative models have enabled the creation of contents that are indistinguishable from those taken from the nature. Open-source development of such models raised concerns about the risks in their misuse for malicious purposes. One potential risk mitigation strategy is to attribute generative models via fingerprinting. Current fingerprinting methods exhibit significant tradeoff between robust attribution accuracy and generation quality, and also lack designing principles to improve this tradeoff. This paper investigates the use of latent semantic dimensions as fingerprints, from where we can analyze the effects of design variables, including the choice of fingerprinting dimensions, strength, and capacity, on the accuracy-quality tradeoff. Compared with previous SOTA, our method requires minimum computation and is more applicable to large-scale models. We use StyleGAN2 and the latent diffusion model to demonstrate the efficacy of our method.

翻译：生成模型已能够创造出与自然内容难以区分的作品。此类模型的开源开发引发了对恶意滥用风险的担忧。一种潜在的风险缓解策略是通过指纹技术对生成模型进行归因。现有指纹方法在鲁棒归因准确率与生成质量之间呈现显著权衡，且缺乏改进此权衡的设计原则。本文研究将潜在语义维度作为指纹的用法，由此可分析设计变量（包括指纹维度、强度及容量的选择）对准确率-质量权衡的影响。与先前最先进方法相比，本方法计算需求最小，更适用于大规模模型。我们使用StyleGAN2和潜在扩散模型验证了本方法的有效性。