Towards Secure and Usable 3D Assets: A Novel Framework for Automatic Visible Watermarking

TL;DR

This paper introduces a novel automated visible watermarking framework for 3D models that optimizes watermark placement and fusion to enhance security and usability, validated through extensive experiments.

Contribution

It presents a new method for automatic watermark placement and fusion in 3D assets, balancing watermark quality and asset utility, using a rigid-body optimization and curvature-matching techniques.

Findings

Outperforms baseline methods in watermark readability and security.

Effectively balances watermark visibility with asset preservation.

Validated on two benchmark 3D datasets.

Abstract

3D models, particularly AI-generated ones, have witnessed a recent surge across various industries such as entertainment. Hence, there is an alarming need to protect the intellectual property and avoid the misuse of these valuable assets. As a viable solution to address these concerns, we rigorously define the novel task of automated 3D visible watermarking in terms of two competing aspects: watermark quality and asset utility. Moreover, we propose a method of embedding visible watermarks that automatically determines the right location, orientation, and number of watermarks to be placed on arbitrary 3D assets for high watermark quality and asset utility. Our method is based on a novel rigid-body optimization that uses back-propagation to automatically learn transforms for ideal watermark placement. In addition, we propose a novel curvature-matching method for fusing the watermark into the 3D model that further improves readability and security. Finally, we provide a detailed experimental analysis on two benchmark 3D datasets validating the superior performance of our approach in comparison to baselines. Code and demo are available.