为扩散模型设计可无缝嵌入的用户水印技术，保质保真。
Towards Effective User Attribution for Latent Diffusion Models via Watermark-Informed Blending
- 通过像素级噪声与增强操作，实现无需修改模型参数的水印嵌入。
- 在不降低图像质量前提下，达到当前最优的感知质量与溯源准确率。
- 适合需要版权保护与用户追踪的生成式AI系统部署场景。
多模态大语言模型的快速发展使得根据文本描述生成超逼真图像成为可能，但同时也引发了未经授权使用带来的严峻问题，制约了其广泛应用。传统水印方法通常需要复杂的集成过程或损害图像质量。为此，我们提出一种新框架——基于水印引导融合的高效用户溯源技术（TEAWIB）。该框架采用即插即用的配置方式，可将用户专属水印无缝嵌入生成模型中。每个用户仅需应用预配置参数集即可完成水印注入，无需修改原始模型参数，且不牺牲图像质量。此外，通过在像素层面嵌入噪声与增强操作，进一步提升了水印的安全性与稳定性。大量实验验证了TEAWIB的有效性，在感知质量和溯源准确性方面均达到当前最优水平。
原文摘要 · Abstract (English)
Rapid advancements in multimodal large language models have enabled the creation of hyper-realistic images from textual descriptions. However, these advancements also raise significant concerns about unauthorized use, which hinders their broader distribution. Traditional watermarking methods often require complex integration or degrade image quality. To address these challenges, we introduce a novel framework Towards Effective user Attribution for latent diffusion models via Watermark-Informed Blending (TEAWIB). TEAWIB incorporates a unique ready-to-use configuration approach that allows seamless integration of user-specific watermarks into generative models. This approach ensures that each user can directly apply a pre-configured set of parameters to the model without altering the original model parameters or compromising image quality. Additionally, noise and augmentation operations are embedded at the pixel level to further secure and stabilize watermarked images. Extensive experiments validate the effectiveness of TEAWIB, showcasing the state-of-the-art performance in perceptual quality and attribution accuracy.
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