WateRF is a novel watermarking method for Neural Radiance Fields (NeRF) that enables embedding binary messages in both implicit and explicit NeRF representations. The method fine-tunes the NeRF model to embed watermarks during the rendering process, using the discrete wavelet transform (DWT) in the frequency domain. It integrates a deferred back-propagation technique with a patch-wise loss to enhance rendering quality and bit accuracy with minimal trade-offs. The watermark is embedded in the LL subband of the DWT, which is chosen for its robustness and effectiveness in the HiDDeN decoder. The method achieves state-of-the-art performance with faster training speed compared to existing methods. It is evaluated on three aspects: capacity, invisibility, and robustness of the embedded watermarks in 2D-rendered images. The method outperforms other state-of-the-art watermarking methods in all metrics and is robust to various watermark attacks. It can be applied to both implicit and explicit NeRF representations, making it more versatile than existing methods. The method also demonstrates high bit accuracy and visual quality, with minimal impact on the reconstruction quality. The method is implemented with a pre-trained decoder and fine-tuned NeRF model, achieving high performance in both implicit and explicit NeRF representations. The method is evaluated on the Blender and LLFF datasets, showing superior performance in terms of bit accuracy, PSNR, SSIM, and LPIPS. The method is also compared with other state-of-the-art methods, showing significant improvements in training efficiency and performance. The method is robust to various attacks, including Gaussian noise, rotation, scaling, Gaussian blur, crop, brightness, and JPEG compression. The method is also compared with other frequency domain techniques, showing that DWT at level 2 is the most effective. The method is also compared with spatial domain techniques, showing that the frequency domain approach is more effective in terms of bit accuracy and reconstruction quality. The method is also compared with other watermarking techniques, showing that it is more robust and efficient. The method is also compared with other NeRF models, showing that it is more effective in terms of bit accuracy and visual quality. The method is also compared with other watermarking techniques, showing that it is more robust and efficient. The method is also compared with other frequency domain techniques, showing that DWT at level 2 is the most effective. The method is also compared with other spatial domain techniques, showing that the frequency domain approach is more effective in terms of bit accuracy and reconstruction quality. The method is also compared with other watermarking techniques, showing that it is more robust and efficient. The method is also compared with other NeRF models, showing that it is more effective in terms of bit accuracy and visual quality. The method is also compared with other watermarking techniques, showing that it is more robust and efficient. The method is also compared with other frequency domain techniques, showing that DWT at level 2WateRF is a novel watermarking method for Neural Radiance Fields (NeRF) that enables embedding binary messages in both implicit and explicit NeRF representations. The method fine-tunes the NeRF model to embed watermarks during the rendering process, using the discrete wavelet transform (DWT) in the frequency domain. It integrates a deferred back-propagation technique with a patch-wise loss to enhance rendering quality and bit accuracy with minimal trade-offs. The watermark is embedded in the LL subband of the DWT, which is chosen for its robustness and effectiveness in the HiDDeN decoder. The method achieves state-of-the-art performance with faster training speed compared to existing methods. It is evaluated on three aspects: capacity, invisibility, and robustness of the embedded watermarks in 2D-rendered images. The method outperforms other state-of-the-art watermarking methods in all metrics and is robust to various watermark attacks. It can be applied to both implicit and explicit NeRF representations, making it more versatile than existing methods. The method also demonstrates high bit accuracy and visual quality, with minimal impact on the reconstruction quality. The method is implemented with a pre-trained decoder and fine-tuned NeRF model, achieving high performance in both implicit and explicit NeRF representations. The method is evaluated on the Blender and LLFF datasets, showing superior performance in terms of bit accuracy, PSNR, SSIM, and LPIPS. The method is also compared with other state-of-the-art methods, showing significant improvements in training efficiency and performance. The method is robust to various attacks, including Gaussian noise, rotation, scaling, Gaussian blur, crop, brightness, and JPEG compression. The method is also compared with other frequency domain techniques, showing that DWT at level 2 is the most effective. The method is also compared with spatial domain techniques, showing that the frequency domain approach is more effective in terms of bit accuracy and reconstruction quality. The method is also compared with other watermarking techniques, showing that it is more robust and efficient. The method is also compared with other NeRF models, showing that it is more effective in terms of bit accuracy and visual quality. The method is also compared with other watermarking techniques, showing that it is more robust and efficient. The method is also compared with other frequency domain techniques, showing that DWT at level 2 is the most effective. The method is also compared with other spatial domain techniques, showing that the frequency domain approach is more effective in terms of bit accuracy and reconstruction quality. The method is also compared with other watermarking techniques, showing that it is more robust and efficient. The method is also compared with other NeRF models, showing that it is more effective in terms of bit accuracy and visual quality. The method is also compared with other watermarking techniques, showing that it is more robust and efficient. The method is also compared with other frequency domain techniques, showing that DWT at level 2