Rethinking Artistic Copyright Infringements In the Era Of Text-to-Image Generative Models
Mazda Moayeri Sriram Balasubramanian Samyadeep Basu Priyatham Kattakinda Atoosa Chegini Robert Brauneis Soheil Feizi
The advent of text-to-image generative models has led artists to worry that their individual styles may be copied, creating a pressing need to reconsider the lack of protection for artistic styles under copyright law. This requires answering challenging questions, like what defines style and what constitutes style infringment. In this work, we build on prior legal scholarship to develop an automatic and interpretable framework to \emph{quantitatively} assess style infringement. Our methods hinge on a simple logical argument: if an artist's works can consistently be recognized as their own, then they have a unique style. Based on this argument, we 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 corpus of works from hundreds of artists, and (ii) recognize if the identified style reappears in generated images. We then apply ArtSavant in an empirical study to quantify the prevalence of artistic style copying across 3 popular text-to-image generative models, finding that under simple prompting, $20\\%$ of $372$ prolific artists studied appear to have their styles be at risk of copying by today's generative models. Our findings show that prior legal arguments can be operationalized in quantitative ways, towards more nuanced examination of the issue of artistic style infringements.
evaluating copying, copyright, generative ai, text-to-image, ai art, law, interpretability, social impact
Rethinking how we define artistic style and characterize style infringement, with an efficient and interpretable quantitative framework based in legal scholarship