Rethinking Artistic Copyright Infringements In the Era Of Text-to-Image Generative Models
Mazda Moayeri, Sriram Balasubramanian, Samyadeep Basu, Priyatham Kattakinda, Atoosa Malemir Chegini, Robert Brauneis, Soheil Feizi
Abstract
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 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. Published as a conference paper at ICLR 2025 You have a unique and recognizable style! We can identify your style (over the style of 372 other artists) in 88.37% of your works. This puts you in the top 83.6% percentile of artists in recognizability. Your style is detected in works generated by Stable Diffusion. When prompting a gen AI model to copy you, the resultant images exhibit your style more than 372 other artists 70.34% of the time. ArtSavant Report for Canaletto We find stylistic elements unique to you that reappear in generated images. We identify some tag signatures (set of stylistic elements that frequently co-occur only in your work) that also appear in generated images. Here's an example; click to see more.
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Cited by top-tier papers4
- Do LLMs Know to Respect Copyright Notice?Jialiang Xu, Shenglan Li, Zhaozhuo Xu, Denghui ZhangEMNLP 2024 · 1 citation
- StyleSentinel: Reliable Artistic Copyright Verification via Stylistic FingerprintsLingxiao Chen, Liqin Wang, Wei LuAAAI 2026
- Anchored Decoding: Provably Reducing Copyright Risk for Any Language ModelJacqueline He, Jonathan Hayase, Scott Yih, Sewoong Oh et al.ICML 2026
- COPYLENS: Towards Copyrighted Characters Infringement Detection via Copyright-Aware Prompt LearningYaoyu Jin, Xiaochun Yang, Hong Liu, Leixia Wang et al.CVPR 2026
Builds on18
- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh et al.ICML 2021 · 47,906 citations
- High-Resolution Image Synthesis with Latent Diffusion ModelsRobin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser et al.CVPR 2022 · 13,123 citations
- Photorealistic Text-to-Image Diffusion Models with Deep Language UnderstandingChitwan Saharia, William Chan, Saurabh Saxena, Lala Li et al.NeurIPS 2022 · 8,965 citations
- Emerging Properties in Self-Supervised Vision TransformersMathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou et al.ICCV 2021 · 8,921 citations
- SDXL: Improving Latent Diffusion Models for High-Resolution Image SynthesisDustin Podell, Zion English, Kyle Lacey, Andreas Blattmann et al.ICLR 2024 · 4,569 citations
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