An Extensive Analysis on Optimized 3D Watermarking Using Enhanced Clustering Techniques

Abstract

The rapid advancement of technology and increased internet usage have significantly contributed to the development of multimedia technologies, such as computer games and computer graphics. A key aspect of these advancements is the creation and widespread use of 3D models. However, this rapid development has also led to issues like illegal usage and theft of 3D models. Ensuring ownership protection is crucial to addressing these challenges. An effective method for copyright protection and preserving the integrity of 3D mesh models is digital watermarking. 3D watermarking is essential for safeguarding the copyright of 3D mesh models. This proposed study introduces an innovative approach that combines three powerful techniques. Fuzzy C-Means (FCM), Gaussian Mixture Model (GMM), and K-Means clustering to cluster similar vertices and embed watermarks. These techniques are chosen due to their simplicity, efficiency and accuracy that are proven in other domains. In the proposed work the cluster size is varied for each algorithm, and the watermark is embedded in the largest cluster, specifically in the ‘Z’ coordinate of the model. To test the robustness of the watermark, common attacks and geometric attacks are applied to the watermarked models. The embedded watermark is extracted with an NCC value close to 1 for all clustering algorithms, indicating high accuracy. The study evaluates the imperceptibility and robustness of the watermarking method, achieving high Peak Signal To Noise Ratio (PSNR) values and low Root Mean Square Error (RMSE), which demonstrate the effectiveness of the novel approach. The results indicate that the method attains excellent Normalized Correlation Coefficient (NCC) values, confirming its reliability and robustness.

Keywords

• 3D model watermarking
• FCM
• GMM
• K-Means
• robustness
• imperceptibility