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.

本文提出了一种基于可训练噪声的新型 NeRF 隐写术方法 ——Noise-NeRF，并且采用自适应像素选择策略和像素扰动策略来提高隐写术质量和效率。在开源数据集上的广泛实验表明 Noise-NeRF 在隐写术质量、渲染质量和超分辨率图像隐写术方面都达到了最先进的性能。