Neural radiance fields (NeRF) have been proposed as an innovative 3D representation method. While attracting lots of attention, NeRF faces critical issues such as information confidentiality and security. Steganography is a technique used to embed information in another object as a means of protecting information security. Currently, there are few related studies on NeRF steganography, facing challenges in low steganography quality, model weight damage, and a limited amount of steganographic information. This paper proposes a novel NeRF steganography method based on trainable noise: Noise-NeRF. Furthermore, we propose the Adaptive Pixel Selection strategy and Pixel Perturbation strategy to improve the steganography quality and efficiency. The extensive experiments on open-source datasets show that Noise-NeRF provides state-of-the-art performances in both steganography quality and rendering quality, as well as effectiveness in super-resolution image steganography.

翻译：摘要：神经辐射场（NeRF）已被提出作为一种创新的三维表示方法。尽管吸引了大量关注，但NeRF面临信息保密性和安全性等关键问题。隐写术是一种将信息嵌入另一对象以保护信息安全的技术。目前，关于NeRF隐写术的相关研究较少，面临着隐写质量低、模型权重损坏以及隐写信息量有限等挑战。本文提出了一种基于可训练噪声的新型NeRF隐写方法：Noise-NeRF。此外，我们提出了自适应像素选择策略和像素扰动策略以提高隐写质量和效率。在开源数据集上的大量实验表明，Noise-NeRF在隐写质量和渲染质量方面均达到了最先进的性能，并在超分辨率图像隐写中展现出有效性。