Identifying Prompted Artist Names from Generated Images

• URL: http://arxiv.org/abs/2507.18633v1
• Date: Thu, 24 Jul 2025 17:59:44 GMT
• Title: Identifying Prompted Artist Names from Generated Images
• Authors: Grace Su, Sheng-Yu Wang, Aaron Hertzmann, Eli Shechtman, Jun-Yan Zhu, Richard Zhang,
• Abstract summary: A common and controversial use of text-to-image models is to generate pictures by explicitly naming artists.<n>We introduce a benchmark for prompted-artist recognition.<n>The dataset contains 1.95M images covering 110 artists.
• Score: 59.34482128911978
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A common and controversial use of text-to-image models is to generate pictures by explicitly naming artists, such as "in the style of Greg Rutkowski". We introduce a benchmark for prompted-artist recognition: predicting which artist names were invoked in the prompt from the image alone. The dataset contains 1.95M images covering 110 artists and spans four generalization settings: held-out artists, increasing prompt complexity, multiple-artist prompts, and different text-to-image models. We evaluate feature similarity baselines, contrastive style descriptors, data attribution methods, supervised classifiers, and few-shot prototypical networks. Generalization patterns vary: supervised and few-shot models excel on seen artists and complex prompts, whereas style descriptors transfer better when the artist's style is pronounced; multi-artist prompts remain the most challenging. Our benchmark reveals substantial headroom and provides a public testbed to advance the responsible moderation of text-to-image models. We release the dataset and benchmark to foster further research: https://graceduansu.github.io/IdentifyingPromptedArtists/

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