IBRSteG: 学习一种面向3D高斯泼溅的可泛化隐写框架
IBRSteG: Learning a Generalizable Steganography Framework for 3D Gaussian Splatting
AI总结：
提出IBRSteG，一种可泛化的3DGS隐写框架，通过GAS网络学习场景无关的嵌入函数，将秘密3D高斯属性注入覆盖场景，无需逐场景微调即可重构隐写场景，实现高视觉质量、大容量和安全性。
摘要：
深度学习的最新进展显著改善了隐写消息隐藏。然而，为3D高斯泼溅（3DGS）设计一种可泛化的隐写方法，能够嵌入有意义的3D场景内容仍然具有挑战性。在本文中，我们提出IBRSteG，一种用于3DGS隐写的可泛化框架，能够在隐写场景中实现秘密场景的不可检测隐藏。与现有方法中参数生成与特定场景刚性耦合不同，我们将3D隐写公式化为前馈3D高斯嵌入过程，该过程可泛化到不同的3DGS场景。为实现这一点，我们引入了GAS（高斯属性隐写器），一个通过学习场景无关嵌入函数的网络，通过将秘密3D高斯点的属性注入覆盖场景，直接重构隐写场景，无需逐场景微调或优化。通过将3D高斯转换为这些结构化属性，这些属性与2D学习范式兼容并受益于其结构化特性，从而增强了对未见3DGS场景的泛化能力。在已建立数据集上的大量实验表明，IBRSteG能够以高视觉质量有效隐藏不同场景，并实现了优越的容量和安全性。代码可在https://github.com/LingXiang2023/IBRSteG获取。
英文摘要：
Recent advances in deep learning have notably improved steganographic message hiding. However, designing a generalizable steganographic approach for 3D Gaussian Splatting (3DGS) that can embed meaningful 3D scene content remains challenging. In this paper, we propose IBRSteG, a generalizable framework for 3DGS steganography that enables undetectable concealment of secret scenes within a steganographic scene. Unlike existing approaches whose parameter generation is rigidly coupled with the specific scene, we formulate 3D steganography as a feed-forward 3D Gaussian embedding process that generalizes across different 3DGS scenes. To realize this, we introduce GAS (Gaussian Attributes Steganographer), a network that learns a scene-independent embedding function by injecting the attributes of secret 3D Gaussian points into a cover scene, thereby directly reconstructing the steganographic scenes without per-scene finetuning or optimization. By transforming 3D Gaussian into these structured attributes, these attributes are compatible with 2D learning paradigms and benefit from their structured nature, thereby enhancing generalization to unseen 3DGS scenes. Extensive experiments on established datasets demonstrate that IBRSteG can effectively conceal different scenes with high visual quality, and achieves superior capacity and security. Code is available at https://github.com/LingXiang2023/IBRSteG.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
