Generative models have seen an explosion in popularity with the release of
huge generative Diffusion models like Midjourney and Stable Diffusion to the
public. Because of this new ease of access, questions surrounding the automated
collection of data and issues regarding content ownership have started to
build. In this paper we present new work which aims to provide ways of
protecting content when shared to the public. We show that a generative
Diffusion model trained on data that has been imperceptibly watermarked will
generate new images with these watermarks present. We further show that if a
given watermark is correlated with a certain feature of the training data, the
generated images will also have this correlation. Using statistical tests we
show that we are able to determine whether a model has been trained on marked
data, and what data was marked. As a result our system offers a solution to
protect intellectual property when sharing content online.

通过使用生成扩散模型对数据进行水印处理，我们展示了一种保护共享内容知识产权的方法，使得生成的图像带有水印并与训练数据特征相关联，以及通过统计测试确定模型是否使用了带有水印的数据。