Watermark Embedder Optimization for 3D Mesh Objects Using Classification Based Approach

This paper presents a novel 3D mesh watermarking scheme that utilizes a support vector machine(SVM) based classifier for watermark insertion. Artificial intelligence(AI)based approaches have been employed by watermarking algorithms for various host mediums such as images, audio, and video. However, AI based techniques are yet to be explored by researchers in the 3D domain for watermark insertion and extraction processes. Contributing towards this end, the proposed approach employs a binary SVM to classify vertices as appropriate or inappropriate candidates for watermark insertion. The SVM is trained with feature vectors derived from the curvature estimates of a 1-ring neighborhood of vertices taken from normalized 3D meshes. A geometry-based non-blind approach is used by the watermarking algorithm. The robustness of proposed technique is evaluated experimentally by simulating attacks such as mesh smoothing, cropping and noise addition.