Neuro-OSVETA: A Robust Watermarking of 3D Meshes

TL;DR

Neuro-OSVETA introduces a neural network-enhanced method for robust, blind watermarking of 3D meshes, improving vertex stability detection and resistance to mesh simplification attacks.

Contribution

It replaces heuristic vertex selection in OSVETA with neural networks, enhancing accuracy and robustness in 3D mesh watermarking.

Findings

Improved vertex stability detection under mesh simplification.

Enhanced watermark robustness and accuracy.

Significant reduction in vertex deletion probability.

Abstract

Best and practical watermarking schemes for copyright protection of 3D meshes are required to be blind and robust to attacks and errors. In this paper, we present the latest developments in 3D blind watermarking with a special emphasis on our Ordered Statistics Vertex Extraction and Tracing Algorithm (OSVETA) algorithm and its improvements. OSVETA is based on a combination of quantization index modulation (QIM) and error correction coding using novel ways for judicial selection of mesh vertices which are stable under mesh simplification, and the technique we propose in this paper offers a systematic method for vertex selection based on neural networks replacing a heuristic approach in the OSVETA. The Neuro-OSVETA enables a more precise mesh geometry estimation and better curvature and topological feature estimation. These enhancements result in a more accurate identification of stable vertices resulting in significant reduction of deletion probability.