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Neural radiance fields (NeRF) have been proposed as an innovative 3Drepresentation method. While attracting lots of attention, NeRF faces criticalissues such as information confidentiality and security. Steganography is atechnique used to embed information in another object as a means of protectinginformation security. Currently, there are few related studies on NeRFsteganography, facing challenges in low steganography quality, model weightdamage, and a limited amount of steganographic information. This paper proposesa novel NeRF steganography method based on trainable noise: Noise-NeRF.Furthermore, we propose the Adaptive Pixel Selection strategy and PixelPerturbation strategy to improve the steganography quality and efficiency. Theextensive experiments on open-source datasets show that Noise-NeRF providesstate-of-the-art performances in both steganography quality and renderingquality, as well as effectiveness in super-resolution image steganography.

Noise-NeRF: Hide Information in Neural Radiance Fields using Trainable Noise