Noise-NeRF: Hide Information in Neural Radiance Fields using Trainable
  Noise
Qinglong Huang Yong Liao Yanbin Hao Pengyuan Zhou
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.