AI Bibliography
AI Bibliography |
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Shen, X., Qu, Y., Backes, M., & Zhang, Y. (2023). Prompt stealing attacks against text-to-image generation models. arXiv preprint arXiv:2302.09923. |
| Resource type: Journal Article BibTeX citation key: Shen2023 View all bibliographic details  |
Categories: Artificial Intelligence, Computer Science, Data Sciences, Engineering, General Subcategories: Big data, Deep learning, Machine learning, Machine recognition Creators: Backes, Qu, Shen, Zhang Publisher: Collection: arXiv preprint arXiv:2302.09923 |
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| Abstract |
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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 propose 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 direct violate the intellectual property and privacy of prompt engineers and also jeopardize the business model of prompt trading marketplaces. We first perform a large-scale 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. We then propose the first learning-based prompt stealing attack, PromptStealer, and demonstrate its superiority over two baseline methods quantitatively and qualitatively. We also make some initial attempts to defend PromptStealer. In general, our study uncovers a new attack surface in the ecosystem created by the popular text-to-image generation models. We hope our results can help to mitigate the threat. To facilitate research in this field, we will share our dataset and code with the community.
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