17 June 2024 Stega4NeRF: cover selection steganography for neural radiance fields
Weina Dong, Jia Liu, Lifeng Chen, Wenquan Sun, Xiaozhong Pan
Author Affiliations +
Abstract

The implicit neural representation of visual data (such as images, videos, and 3D models) has become a current hotspot in computer vision research. This work proposes a cover selection steganography scheme for neural radiance fields (NeRFs). The message sender first trains an NeRF model selecting any viewpoint in 3D space as the viewpoint key Kv, to generate a unique secret viewpoint image. Subsequently, a message extractor is trained using overfitting to establish a one-to-one mapping between the secret viewpoint image and the secret message. To address the issue of securely transmitting the message extractor in traditional steganography, the message extractor is concealed within a hybrid model performing standard classification tasks. The receiver possesses a shared extractor key Ke, which is used to recover the message extractor from the hybrid model. Then the secret viewpoint image is obtained by NeRF through the viewpoint key Kv, and the secret message is extracted by inputting it into the message extractor. Experimental results demonstrate that the trained message extractor achieves high-speed steganography with a large capacity and attains a 100% message embedding. Additionally, the vast viewpoint key space of NeRF ensures the concealment of the scheme.

© 2024 SPIE and IS&T
Weina Dong, Jia Liu, Lifeng Chen, Wenquan Sun, and Xiaozhong Pan "Stega4NeRF: cover selection steganography for neural radiance fields," Journal of Electronic Imaging 33(3), 033031 (17 June 2024). https://doi.org/10.1117/1.JEI.33.3.033031
Received: 17 January 2024; Accepted: 22 May 2024; Published: 17 June 2024
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KEYWORDS
Education and training

Steganography

Data modeling

3D modeling

Neural networks

Cameras

Receivers

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