Identifying Prompted Artist Names from Generated Images

TL;DR

This paper introduces a benchmark dataset and evaluation methods for identifying artist names invoked in prompts from generated images, addressing challenges in artist attribution and model moderation.

Contribution

It provides a large-scale dataset, diverse evaluation settings, and analysis of various methods for prompted-artist recognition in generated images.

Findings

Supervised and few-shot models perform well on seen artists and complex prompts.

Style descriptors transfer better when artist styles are distinctive.

Multi-artist prompts are the most challenging for recognition.

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/