A Dual Blind Watermarking Method for 3D Models Based on Normal Features.

Area of Science:

• Computer Science
• Digital Signal Processing
• Geometric Modeling

Background:

• Digital watermarking is crucial for protecting 3D model data.
• Achieving blind detection and robustness are significant challenges in 3D model watermarking.

Purpose of the Study:

• To propose a dual blind watermarking method for 3D models.
• To enhance watermark robustness against common attacks.
• To enable blind detection of embedded watermarks.

Main Methods:

• A dual blind watermarking method is proposed using normal features of centroid's first-ring neighboring points.
• A local spherical coordinate system is constructed using two distinct normal vectors.
• Two watermarks are embedded: a pattern watermark and a random binary sequence watermark.

Main Results:

• The proposed method successfully achieves blind detection of dual watermarks.
• The watermarking technique demonstrates robustness against common attacks including translation, rotation, scaling, cropping, simplification, smoothing, noise, and vertex reordering.
• Experimental results validate the effectiveness of the dual blind watermarking approach.

Conclusions:

• The developed dual blind watermarking method effectively protects 3D model data.
• The approach offers a viable solution for secure 3D model data management, addressing key challenges in blind detection and robustness.
• This research contributes to advancing digital watermarking techniques for 3D models.