  Copyright infringement may occur when a generative model produces samples
substantially similar to some copyrighted data that it had access to during the
training phase. The notion of access usually refers to including copyrighted
samples directly in the training dataset, which one may inspect to identify an
infringement. We argue that such visual auditing largely overlooks a concealed
copyright infringement, where one constructs a disguise that looks drastically
different from the copyrighted sample yet still induces the effect of training
Latent Diffusion Models on it. Such disguises only require indirect access to
the copyrighted material and cannot be visually distinguished, thus easily
circumventing the current auditing tools. In this paper, we provide a better
understanding of such disguised copyright infringement by uncovering the
disguises generation algorithm, the revelation of the disguises, and
importantly, how to detect them to augment the existing toolbox. Additionally,
we introduce a broader notion of acknowledgment for comprehending such indirect
access. Our code is available at
https://github.com/watml/disguised_copyright_infringement.
