GaussianSeal：为3D高斯生成模型植入自适应水印
GaussianSeal: Rooting Adaptive Watermarks for 3D Gaussian Generation Model
AI总结：
本文提出GaussianSeal，首个针对3DGS生成模型的比特水印框架，通过自适应比特调制模块嵌入网络，实现高精度版权标识解码，优于后处理方法并保持生成质量。
摘要：
随着AIGC技术的发展，模型生成的模态已从图像和视频扩展到3D对象，导致越来越多的工作聚焦于3D高斯泼溅（3DGS）生成模型。现有关于生成模型版权保护的研究主要集中在图像和文本模态的水印技术上，对3D对象生成模型的版权保护探索甚少。在本文中，我们提出了首个针对3DGS生成模型的比特水印框架，命名为GaussianSeal，旨在从生成的3DGS渲染输出中解码作为版权标识的比特。通过将自适应比特调制模块纳入生成模型，并以自适应方式嵌入网络块中，我们在保持模型输出保真度的同时，以极小的训练开销实现了高精度的比特解码。实验表明，我们的方法优于针对3DGS对象的后处理水印方法，在水印解码准确率上表现出更优的性能，并保持了生成结果的质量。
英文摘要：
With the advancement of AIGC technologies, the modalities generated by models have expanded from images and videos to 3D objects, leading to an increasing number of works focused on 3D Gaussian Splatting (3DGS) generative models. Existing research on copyright protection for generative models has primarily concentrated on watermarking in image and text modalities, with little exploration into the copyright protection of 3D object generative models. In this paper, we propose the first bit watermarking framework for 3DGS generative models, named GaussianSeal, to enable the decoding of bits as copyright identifiers from the rendered outputs of generated 3DGS. By incorporating adaptive bit modulation modules into the generative model and embedding them into the network blocks in an adaptive way, we achieve high-precision bit decoding with minimal training overhead while maintaining the fidelity of the model's outputs. Experiments demonstrate that our method outperforms post-processing watermarking approaches for 3DGS objects, achieving superior performance of watermark decoding accuracy and preserving the quality of the generated results.
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