{"ID":2891039,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.18633","arxiv_id":"2507.18633","title":"Identifying Prompted Artist Names from Generated Images","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/","short_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 image...","url_abs":"https://arxiv.org/abs/2507.18633","url_pdf":"https://arxiv.org/pdf/2507.18633v1","authors":"[\"Grace Su\",\"Sheng-Yu Wang\",\"Aaron Hertzmann\",\"Eli Shechtman\",\"Jun-Yan Zhu\",\"Richard Zhang\"]","published":"2025-07-24T17:59:44Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}