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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