Original Paper: https://arxiv.org/abs/2302.09923

By: Xinyue Shen, Yiting Qu, Michael Backes, Yang Zhang

Abstract:

Text-to-Image generation models have revolutionized the artwork design process and enabled anyone to create high-quality images by entering text descriptions called prompts. Creating a high-quality prompt that consists of a subject and several modifiers can be time-consuming and costly. In consequence, a trend of trading high-quality prompts on specialized marketplaces has emerged. In this paper, we perform the first study on understanding the threat of a novel attack, namely prompt stealing attack, which aims to steal prompts from generated images by text-to-image generation models. Successful prompt stealing attacks directly violate the intellectual property of prompt engineers and jeopardize the business model of prompt marketplaces. We first perform a systematic analysis on a dataset collected by ourselves and show that a successful prompt stealing attack should consider a prompt's subject as well as its modifiers. Based on this observation, we propose a simple yet effective prompt stealing attack, PromptStealer. It consists of two modules: a subject generator trained to infer the subject and a modifier detector for identifying the modifiers within the generated image. Experimental results demonstrate that PromptStealer is superior over three baseline methods, both quantitatively and qualitatively. We also make some initial attempts to defend PromptStealer. In general, our study uncovers a new attack vector within the ecosystem established by the popular text-to-image generation models. We hope our results can contribute to understanding and mitigating this emerging threat.

Summary Notes

Understanding Prompt Stealing Attacks and Protecting AI-Generated Art

The digital art world is undergoing a transformation with text-to-image models, which allow users to create images from text prompts.

This innovation has made art creation more accessible and led to the emergence of a market for high-quality prompts. However, this progress has also introduced new security risks, notably prompt stealing attacks.

This blog post explores the problem of prompt stealing attacks and presents solutions for AI Engineers in enterprise settings to mitigate these risks.

The Role of Prompts in AI-Generated Art

Prompts are essential in AI-generated art, directing models to produce images that reflect users' ideas.

Crafting effective prompts has become a specialized skill, giving rise to the role of the prompt engineer.

These professionals are key to the success of digital art, leveraging their skills to communicate with AI models to produce desired outcomes.

Introduction to PromptStealer

PromptStealer represents a significant risk to AI-generated art, capable of deducing the original prompts from images.

This tool uses a subject generator and modifier detector to reverse-engineer prompts with high accuracy. Its effectiveness over other methods was demonstrated in research, highlighting the threat of prompt theft.

Defending with PromptShield

To counter prompt theft, researchers have developed PromptShield, a defense strategy that introduces small, optimized changes to images.

These alterations aim to disrupt the prompt inference process, thus protecting the original prompts. While still early in development, PromptShield is a promising approach to safeguarding creators' intellectual property.

Evaluating the Threat: The Lexica-Dataset

The research utilizes the Lexica-Dataset, a comprehensive compilation of prompts and images from Lexica, to assess the threat of PromptStealer and the efficacy of PromptShield.

This dataset was crucial for understanding the role of different elements in AI-generated art and for developing defensive strategies.

Ethical and Implications

Prompt stealing raises ethical issues, particularly concerning the intellectual property rights of artists and creators.

The research team has approached these concerns responsibly, advocating for careful data use and transparent findings. This approach aims to foster ethical practices in handling such sensitive issues.

Looking Ahead

The fight against prompt stealing is ongoing, with the need for more effective defenses. Future research should explore additional text-to-image models and develop new strategies to protect creative works.

As AI technology advances, so must the measures to secure the intellectual and creative assets it produces.

Conclusion

The study on prompt stealing attacks offers valuable insights into a new challenge for the AI-generated art community.

It introduces the threats of PromptStealer and the defensive potential of PromptShield, setting the stage for future security efforts.

For AI Engineers, this research is crucial, offering guidance on protecting AI-generated art against unauthorized exploitation.