MaxMark：通过鲁棒可逆潜变量嵌入实现高容量扩散原生水印
MaxMark: High-Capacity Diffusion-Native Watermarking via Robust and Invertible Latent Embedding
对做扩散模型溯源与版权保护的研究者，这篇给出了在潜噪声上做高容量嵌入并用可逆网络把带水印潜变量映射回近似高斯的做法，可直接复用到 LDM 生成图像的隐写与来源追踪流程中。
原文依据 a distribution transformation module that maps the watermarked latent back to an approximate Gaussian
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论扩散原生水印通过将信息直接嵌入生成过程，为追踪潜扩散模型（LDM）生成的图像提供了更安全可靠的方式。然而，现有方法存在一个根本性局限：其嵌入容量极小。我们提出 MaxMark，一个高容量水印框架，支持向生成图像中嵌入丰富的水印消息。MaxMark 包含两个组件：一个鲁棒水印嵌入模块，用于增强秘密消息并将其放置到潜噪声的可靠区域；以及一个分布变换模块，将带水印的潜变量映射回近似高斯分布，从而确保与扩散过程的兼容性并保持图像保真度。该分布变换由可逆神经网络（INN）实现，其精确可逆的结构支持精确恢复与高效训练。实验表明，MaxMark 在容量、鲁棒性和不可感知性上均超越先前方法，在大水印载荷下比特准确率提升最高达 46%。
英文原文摘要 1118 字符
Diffusion-native watermarking provides a more secure and reliable way to trace images from latent diffusion models (LDMs) by embedding information directly into the generative process. However, existing methods suffer from a fundamental limitation: their embedding capacity is extremely small. We introduce MaxMark, a high-capacity watermarking framework that supports embed rich watermark messages into generated images. MaxMark uses two components: a robust watermark embedding module that enhance the secret message and places them into reliable regions of the latent noise, and a distribution transformation module that maps the watermarked latent back to an approximate Gaussian, ensuring compatibility with the diffusion process and preserving image fidelity. The distribution transformation is implemented with an invertible neural network (INN), whose exactly reversible structure enables precise recovery and efficient training. Experiments show that MaxMark surpasses prior methods in capacity, robustness, and imperceptibility, achieving up to a 46% improvement in bit accuracy for large watermark payloads.