Identifying Prompted Artist Names from Generated Images
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912499946749952 |
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| author | Su, Grace Wang, Sheng-Yu Hertzmann, Aaron Shechtman, Eli Zhu, Jun-Yan Zhang, Richard |
| author_facet | Su, Grace Wang, Sheng-Yu Hertzmann, Aaron Shechtman, Eli Zhu, Jun-Yan Zhang, Richard |
| contents | 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/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_18633 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Identifying Prompted Artist Names from Generated Images Su, Grace Wang, Sheng-Yu Hertzmann, Aaron Shechtman, Eli Zhu, Jun-Yan Zhang, Richard Computer Vision and Pattern Recognition 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/ |
| title | Identifying Prompted Artist Names from Generated Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.18633 |