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Prompt and Circumstances: Evaluating the Efficacy of Human Prompt Inference in AI-Generated Art

arXiv 2601.17379 ↗First posted 24 Jan 2026cs.CR cs.AIcited by 0

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Abstract (arXiv)

The emerging field of AI-generated art has witnessed the rise of prompt marketplaces, where creators can purchase, sell, or share prompts to generate 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 bona fide intellectual property, given that humans and AI tools may be able to infer the prompts based on publicly advertised sample images accompanying each prompt on sale. Specifically, our study aims to assess (i) how accurately humans can 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 combined human and AI prompts with the help of a large language model. Although previous research has explored AI-driven prompt inference and protection strategies, our work is the first to incorporate a human subject study and examine collaborative human-AI prompt inference in depth. Our findings indicate that while prompts inferred by humans and prompts inferred through a combined human and AI effort can generate images with a moderate level of similarity, they are not as successful as using the original prompt. Moreover, combining human- and AI-inferred prompts using our suggested merging techniques did not improve performance over purely human-inferred prompts.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 36 references that are in the map. First posted on arXiv 24 Jan 2026.

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References · 20

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  12. 2021Design Guidelines for Prompt Engineering Text-to-Image Generative Modelscited by 216
8 more
  1. 2023Text-to-Image Diffusion Models can be Easily Backdoored through Multimodal Data Poisoningcited by 91
  2. 2023Review of Large Vision Models and Visual Prompt Engineeringcited by 77
  3. 2023Promptify: Text-to-Image Generation through Interactive Prompt Exploration with Large Language Modelscited by 67
  4. 2022Membership Inference Attacks Against Text-to-image Generation Modelscited by 66
  5. 2023Seeing is not always believing: Benchmarking Human and Model Perception of AI-Generated Imagescited by 65
  6. 2023Prompt Stealing Attacks Against Text-to-Image Generation Modelscited by 48
  7. 2024Human vs. AI: A Novel Benchmark and a Comparative Study on the Detection of Generated Images and the Impact of Promptscited by 2
  8. 2025A Picture is Worth a Thousand Prompts? Efficacy of Iterative Human-Driven Prompt Refinement in Image Regeneration Taskscited by 2

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