Kavli Affiliate: Jia Liu
| First 5 Authors: Lifeng Chen, Jia Liu, Yan Ke, Wenquan Sun, Weina Dong
| Summary:
A watermarking algorithm is proposed in this paper to address the copyright
protection issue of implicit 3D models. The algorithm involves embedding
watermarks into the images in the training set through an embedding network,
and subsequently utilizing the NeRF model for 3D modeling. A copyright verifier
is employed to generate a backdoor image by providing a secret perspective as
input to the neural radiation field. Subsequently, a watermark extractor is
devised using the hyperparameterization method of the neural network to extract
the embedded watermark image from that perspective. In a black box scenario, if
there is a suspicion that the 3D model has been used without authorization, the
verifier can extract watermarks from a secret perspective to verify network
copyright. Experimental results demonstrate that the proposed algorithm
effectively safeguards the copyright of 3D models. Furthermore, the extracted
watermarks exhibit favorable visual effects and demonstrate robust resistance
against various types of noise attacks.
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