用合成图像保护数据集，防未经授权生成
ImageSentinel: Protecting Visual Datasets from Unauthorized Retrieval-Augmented Image Generation
- 生成与原数据一致的哨兵图像，嵌入检索密钥
- 可有效检测未授权使用，不影响合法生成质量
- 适合需要数据安全的视觉模型开发者
检索增强图像生成（RAIG）的广泛应用引发了对私有图像数据集被未经授权使用的担忧。尽管此类系统通过参考图像显著提升了生成质量，但如何在RAIG中保护视觉数据集仍具挑战性。传统数字水印方法在复杂特征提取与重组过程中难以保留水印信号。为此，我们提出ImageSentinel，一种面向RAIG场景的新型数据保护框架。该框架通过视觉语言模型生成与原始数据集视觉一致的哨兵图像，并利用随机生成的字符序列作为检索密钥实现保护验证。实验表明，ImageSentinel能有效检测未授权数据使用，同时保持授权应用的生成质量。代码已开源：https://github.com/luo-ziyuan/ImageSentinel。
原文摘要 · Abstract (English)
The widespread adoption of Retrieval-Augmented Image Generation (RAIG) has raised significant concerns about the unauthorized use of private image datasets. While these systems have shown remarkable capabilities in enhancing generation quality through reference images, protecting visual datasets from unauthorized use in such systems remains a challenging problem. Traditional digital watermarking approaches face limitations in RAIG systems, as the complex feature extraction and recombination processes fail to preserve watermark signals during generation. To address these challenges, we propose ImageSentinel, a novel framework for protecting visual datasets in RAIG. Our framework synthesizes sentinel images that maintain visual consistency with the original dataset. These sentinels enable protection verification through randomly generated character sequences that serve as retrieval keys. To ensure seamless integration, we leverage vision-language models to generate the sentinel images. Experimental results demonstrate that ImageSentinel effectively detects unauthorized dataset usage while preserving generation quality for authorized applications. Code is available at https://github.com/luo-ziyuan/ImageSentinel.
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