An Intelligent Learning Approach for Information Hiding in 3D Multimedia

This paper presents a new watermarking algorithm for 3D triangular mesh models that is based on surface curvature estimation and supervised learning. A feedforward backpropagation neural network is adopted for selecting vertices for watermark insertion. A variety of 3D models with varying degrees of surface curvature are used to train and simulate the neural network. An array of neural networks is used for vertices with different valences to achieve higher watermark embedding capacity. A gray scale bitmap image is used as the watermark. The watermark extraction process is informed and needs the original watermark and 3D model. Experimental results evaluate the embedding capacity, imperceptibility and robustness of the proposed algorithm and simulate various attacks including noise addition, smoothing and cropping.