Neural Radiance Field (NeRF) has been proposed as an innovative advancement in 3D reconstruction techniques. However, little research has been conducted on the issues of information confidentiality and security to NeRF, such as steganography. Existing NeRF steganography solutions have shortcomings in low steganography quality, model weight damage, and limited amount of steganographic information. This paper proposes Noise-NeRF, a novel NeRF steganography method employing Adaptive Pixel Selection strategy and Pixel Perturbation strategy to improve the quality and efficiency of steganography via trainable noise. Extensive experiments validate the state-of-the-art performances of Noise-NeRF on both steganography quality and rendering quality, as well as effectiveness in super-resolution image steganography.

翻译：神经辐射场（NeRF）作为一种创新的三维重建技术被提出。然而，针对NeRF的信息保密与安全问题（如隐写术）的研究尚少。现有的NeRF隐写方案存在隐写质量低、模型权重受损以及隐写信息量有限等不足。本文提出Noise-NeRF，一种新颖的NeRF隐写方法，其采用自适应像素选择策略与像素扰动策略，通过可训练噪声提升隐写的质量与效率。大量实验验证了Noise-NeRF在隐写质量与渲染质量方面的先进性能，及其在超分辨率图像隐写中的有效性。