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MarkINeRV: A Robust Watermarking Scheme for neural Representation for Videos Based on invertible neural networks

CMC-COMPUTERS MATERIALS & CONTINUA 2024年第3期80卷 4031-4046页

作者： Sun, Wenquan Liu, Jia Chen, Lifeng Dong, Weina Di, Fuqiang Engn Univ PAP Dept Cryptog Engn Xian 710086 Peoples R China Key Lab Cryptol & Informat Secur Dept Cryptog Engn Xian 710086 Peoples R China

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 inverse processes of a reversible network and employs the same network to achieve embedding and extraction of watermarks. It is just that the information flow is in the opposite direction. Additionally, a video frame quality enhancement module is incorporated to mitigate watermarking information losses in the rendering process and the possibility of malicious attacks during transmission, ensuring the accurate extraction of watermarking information through the invertible network's inverse process. This paper evaluates the accuracy, robustness, and invisibility of MarkINeRV through multiple video datasets. The results demonstrate its efficacy in extracting watermarking information for copyright protection of NeRV. MarkINeRV represents a pioneering investigation into copyright issues surrounding NeRV.

关键词： invertible neural network neural representations for videos watermarking robustness

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