Noise-NeRF: Hide Information in Neural Radiance Fields using Trainable
Noise
- URL: http://arxiv.org/abs/2401.01216v1
- Date: Tue, 2 Jan 2024 14:10:21 GMT
- Title: Noise-NeRF: Hide Information in Neural Radiance Fields using Trainable
Noise
- Authors: Qinglong Huang, Yong Liao, Yanbin Hao, Pengyuan Zhou
- Abstract summary: This paper proposes a novel NeRF steganography method based on trainable noise: Noise-NeRF.
Experiments on open-source datasets show that Noise-NeRF provides state-of-the-art performances in both steganography quality and rendering quality.
- Score: 16.926872641383902
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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