Not the Hero NeRFs Deserve, but the Hero NeRFs Need: CopyRNeRF is an AI Approach That Protects the Copyright of NeRFs

If you have been paying attention to the development in the computer graphics domain, you should be familiar with neural radiance fields (NeRFs). They have emerged as a promising technique for representing 3D scenes and objects. They use deep neural networks to model the appearance of scenes by using a collection of images captured from different viewpoints.

NeRFs can achieve a high-quality synthesis of novel views and enable realistic rendering and even the reconstruction of scenes from sparse and irregularly sampled data. Their ability to handle complex lighting effects has made them a widely studied technique with numerous applications. 

So, instead of capturing our surroundings in 2D, it’s now possible to do it in 3D and delve deeper into the memories. Speaking of capturing, you probably know the famous word that is a big problem for content protection—copyright. People, especially professionals, tend to protect the time and effort they put into capturing beautiful photos or drawing amazing illustrations by copyrighting their products. This way, they can get credit for their time. 

Have you ever wondered about the copyright aspect when it comes to the NeRFs? It is a well-known practice to protect your digital assets by copyrighting them. You take a photo, and you can copyright it; you record a video, and you can copyright it, but what happens to your NeRFs? How can you protect your digital NeRF to prevent unauthorized use or theft? Let us meet with CopyRNeRF.

Training a NeRF model and safeguarding its intellectual property poses significant challenges. One intuitive solution is to embed copyright messages or watermarks directly into rendered samples using existing watermarking approaches. However, this approach only protects the rendered samples and does not safeguard the core NeRF model. This is where NeRF differs from traditional media formats. You need to project the model as well, not just the output.

CopyRNeRF is proposed to tackle this problem. It embeds copyright messages directly into the NeRF model itself. This watermarking process ensures that the copyright information is embedded into the model’s weights, making it accessible only by rendering protected models. To meet the essential standards of watermarking, CopyRNeRF focuses on both invisibility, ensuring that the embedded messages do not cause visual distortions, and robustness, allowing for reliable message extraction under various distortions.

CopyRNeRF can protect model ownership. Source:

Previous attempts using invisible watermarks on 2D images failed to effectively transmit into NeRF models, compromising the robustness of watermark extraction. Instead, CopyRNeRF involves using a watermarked color representation for rendering based on a subset of models. This way, it preserves the base representation and ensures invisibility in the rendering samples. Additionally, spatial information is incorporated into the watermarked color representation, ensuring that the embedded messages remain consistent across different viewpoints rendered from NeRF models.

Overview of CopyRNeRF. Source:

Moreover, to strengthen the robustness of watermark extraction, distortion-resistant rendering is employed during model optimization. A distortion layer ensures reliable watermark extraction even under severe distortions such as blurring, noise, or rotation. Furthermore, a random sampling strategy enhances the protected model’s robustness against different rendering schemes or sampling strategies.

Check out the Paper and Project. All Credit For This Research Goes To the Researchers on This Project. Also, don’t forget to join our 27k+ ML SubReddit, 40k+ Facebook Community, Discord Channel, and Email Newsletter, where we share the latest AI research news, cool AI projects, and more.

The post Not the Hero NeRFs Deserve, but the Hero NeRFs Need: CopyRNeRF is an AI Approach That Protects the Copyright of NeRFs appeared first on MarkTechPost.

Similar Posts

Leave a Reply

Your email address will not be published. Required fields are marked *