Wenquan Sun^1,2, Jia Liu^1,2,*, Weina Dong^1,2, Lifeng Chen^1,2, Fuqiang Di^1,2

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4065-4083, 2024, DOI:10.32604/cmc.2024.053115 - 12 September 2024

Abstract As neural radiance fields continue to advance in 3D content representation, the copyright issues surrounding 3D models oriented towards implicit representation become increasingly pressing. In response to this challenge, this paper treats the embedding and extraction of neural radiance field watermarks as inverse problems of image transformations and proposes a scheme for protecting neural radiance field copyrights using invertible neural network watermarking. Leveraging 2D image watermarking technology for 3D scene protection, the scheme embeds watermarks within the training images of neural radiance fields through the forward process in invertible neural networks and extracts them from… More >

Wenquan Sun^1,2, Jia Liu^1,2,*, Lifeng Chen^1,2, Weina Dong^1,2, Fuqiang Di^1,2

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4031-4046, 2024, DOI:10.32604/cmc.2024.052745 - 12 September 2024

Abstract Recent research advances in implicit neural representation have shown that a wide range of video data distributions are achieved by sharing model weights for Neural Representation for Videos (NeRV). While explicit methods exist for accurately embedding ownership or copyright information in video data, the nascent NeRV framework has yet to address this issue comprehensively. In response, this paper introduces MarkINeRV, a scheme designed to embed watermarking information into video frames using an invertible neural network watermarking approach to protect the copyright of NeRV, which models the embedding and extraction of watermarks as a pair of… More >