INRW4NeRF: implicit neural representation watermarking for neural radiance fields

With the widespread application of neural radiance fields (NeRF) in three-dimensional scene representation and rendering, protecting these models from unauthorized use has become critical. We propose a NeRF watermarking algorithm based on model redundancy, using a pre-training watermark embedding strategy to embed a watermark NeRF model into the carrier NeRF model for copyright protection. First, on an initial implicit representation network of the watermark NeRF, a random neuron inactivation is applied, and the implicit network is trained using a set of image watermark data. Second, neuron pruning techniques are used to remove unimportant weights in the watermark NeRF network, with pruning positions recorded as keys. Finally, a NeRF model for carrier representation is trained while fixing important weights, achieving NeRF watermark embedding. Copyright verifiers can extract the watermark network from the carrier NeRF network using keys to prove ownership. Experimental results show that our method not only maintains high-quality reconstruction effects and achieves lossless extraction of watermarks but also that the watermark type with implicit representation has high robustness and can resist various common attack methods.