  Recent text-to-image generative models such as Stable Diffusion are extremely
adept at mimicking and generating copyrighted content, raising concerns amongst
artists that their unique styles may be improperly copied. Understanding how
generative models copy "artistic style" is more complex than duplicating a
single image, as style is comprised by a set of elements (or signature) that
frequently co-occurs across a body of work, where each individual work may vary
significantly. In our paper, we first reformulate the problem of "artistic
copyright infringement" to a classification problem over image sets, instead of
probing image-wise similarities. We then introduce ArtSavant, a practical
(i.e., efficient and easy to understand) tool to (i) determine the unique style
of an artist by comparing it to a reference dataset of works from 372 artists
curated from WikiArt, and (ii) recognize if the identified style reappears in
generated images. We leverage two complementary methods to perform artistic
style classification over image sets, includingTagMatch, which is a novel
inherently interpretable and attributable method, making it more suitable for
broader use by non-technical stake holders (artists, lawyers, judges, etc).
Leveraging ArtSavant, we then perform a large-scale empirical study to provide
quantitative insight on the prevalence of artistic style copying across 3
popular text-to-image generative models. Namely, amongst a dataset of prolific
artists (including many famous ones), only 20% of them appear to have their
styles be at a risk of copying via simple prompting of today's popular
text-to-image generative models.
