Human-driven iterative prompt refinement improves image regeneration similarity in a 20-person study, while image similarity metrics show only moderate agreement with human rankings.
Promptly Yours? A Human Subject Study on Prompt Inference in AI-Generated Art
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abstract
The emerging field of AI-generated art has witnessed the rise of prompt marketplaces, where creators can purchase, sell, or share prompts for generating unique artworks. These marketplaces often assert ownership over prompts, claiming them as intellectual property. This paper investigates whether concealed prompts sold on prompt marketplaces can be considered as secure intellectual property, given that humans and AI tools may be able to approximately infer the prompts based on publicly advertised sample images accompanying each prompt on sale. Specifically, our survey aims to assess (i) how accurately can humans infer the original prompt solely by examining an AI-generated image, with the goal of generating images similar to the original image, and (ii) the possibility of improving upon individual human and AI prompt inferences by crafting human-AI combined prompts with the help of a large language model. Although previous research has explored the use of AI and machine learning to infer (and also protect against) prompt inference, we are the first to include humans in the loop. Our findings indicate that while humans and human-AI collaborations can infer prompts and generate similar images with high accuracy, they are not as successful as using the original prompt.
fields
cs.AI 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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A Picture is Worth a Thousand Prompts? Efficacy of Iterative Human-Driven Prompt Refinement in Image Regeneration Tasks
Human-driven iterative prompt refinement improves image regeneration similarity in a 20-person study, while image similarity metrics show only moderate agreement with human rankings.