A universal watermarking method that fine-tunes 3DGS parameters against a pre-trained image decoder, extracting ownership messages from rendered views and remaining robust to point-cloud noise, dropout, and cropping.
Noise-NeRF: Hide Information in Neural Radiance Fields using Trainable Noise
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abstract
Neural Radiance Field (NeRF) has been proposed as an innovative advancement in 3D reconstruction techniques. However, little research has been conducted on the issues of information confidentiality and security to NeRF, such as steganography. Existing NeRF steganography solutions have shortcomings in low steganography quality, model weight damage, and limited amount of steganographic information. This paper proposes Noise-NeRF, a novel NeRF steganography method employing Adaptive Pixel Selection strategy and Pixel Perturbation strategy to improve the quality and efficiency of steganography via trainable noise. Extensive experiments validate the state-of-the-art performances of Noise-NeRF on both steganography quality and rendering quality, as well as effectiveness in super-resolution image steganography.
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WATER-GS: Toward Copyright Protection for 3D Gaussian Splatting via Universal Watermarking
A universal watermarking method that fine-tunes 3DGS parameters against a pre-trained image decoder, extracting ownership messages from rendered views and remaining robust to point-cloud noise, dropout, and cropping.