The NeRF Signature: Codebook-Aided Watermarking for Neural Radiance Fields.

Area of Science:

• Computer Vision
• Digital Watermarking
• 3D Graphics

Background:

• Neural Radiance Fields (NeRF) are a powerful 3D content representation.
• Existing NeRF watermarking methods lack model-level considerations, impacting imperceptibility and robustness.
• Copyright protection for NeRF creations is a growing concern.

Purpose of the Study:

• To propose a novel, robust, and imperceptible watermarking method for Neural Radiance Fields (NeRF) at the model level.
• To address the limitations of existing NeRF watermarking techniques.
• To provide effective copyright protection for 3D NeRF content.

Main Methods:

• Introduced NeRF Signature, a model-level watermarking approach.
• Employed Codebook-aided Signature Embedding (CSE) without altering NeRF structure.
• Utilized a joint pose-patch encryption strategy and Complexity-Aware Key Selection (CAKS) for enhanced robustness and imperceptibility.

Main Results:

• NeRF Signature maintains model structure, ensuring imperceptibility and model-level robustness.
• CSE allows flexible embedding of new binary signatures without fine-tuning.
• The proposed method demonstrates superior performance over baseline methods in imperceptibility and robustness.

Conclusions:

• NeRF Signature offers an effective solution for copyright protection of NeRF models.
• The method provides a balance between imperceptibility, robustness, and user convenience.
• This work advances the field of digital watermarking for 3D representations like NeRF.