Watermarking 3D Printing Data Based on Coyote Optimization Algorithm

The main objective of this work is developing 3D printing Data Protection Using Watermarking approach that considers watermarking problem as an optimization problem. 3D objects watermarking inhabits a challenging obstacle. The existence of many 3D objects representations act one reason for this challenge. The 3D models watermarking research state is furthermore in its opening as opposed to published work in video and image watermarking. This work propose a 3D watermarking approach by utilizing Coyote Optimization Algorithm (COA) in optimizing statistical watermarking embedding for 3D mesh model. Coyote optimization algorithm (COA) consider a recent fast and stable meta heuristic algorithm. This proposed approach aims to introduce an intelligent layer on the watermarking process. The approach starts by selecting the best vertices that will carry the watermark bits using k-means clustering method. Followed by watermark embedding step using COA in finding the best local statistical measure modification value. Finally we extract the embedded watermark without any need of the original model. The proposed approach is validated using different visual fidelity and robustness measures. The experimental results of the proposed approach will be compared with other state of the art approaches to prove its superiority in embedding and extraction of watermark bits sequence with respect to both robustness and imperceptibility.