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

2026-01-24

AI Summary

This study investigates whether hidden prompts in AI-generated art constitute identifiable intellectual property, focusing on whether humans or AI systems can reliably infer the original generation prompts from publicly available images alone. To address this, we introduce the first human-subject experiments systematically comparing three strategies—human-only, AI-only, and human-AI collaboration—in prompt inference tasks, complemented by large language model–based prompt fusion and image reconstruction. Our findings show that while prompts inferred by humans and human-AI teams can produce images of moderate visual similarity, they remain significantly inferior to those generated by the original prompts. Moreover, the proposed prompt fusion approach does not outperform human-only inference. This work provides empirical evidence and a novel perspective for delineating intellectual property rights in AI-generated content.

Technology Category

• Natural Language Processing: Prompt Engineering / Prompting
• Humans and AI: Crowd Sourcing and Human Computation
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• prompt inference
• AI-generated art
• intellectual property
• human-AI collaboration
• prompt marketplaces

Innovation

• prompt inference
• human-AI collaboration
• AI-generated art
• intellectual property
• large language model