Peer-reviewed veterinary case report
Dog or cat 3D model copyright protection methods?
By Wang Q et al.·2026·School of Artificial Intelligence, China·View original on Europe PMC →
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Original publication title: Reversible data hiding for 3D mesh models based on spatial polygon prediction and dual sorting.
Plain-English summary
This research focuses on a technique called reversible data hiding (RDH), which is used to securely embed information in digital media while allowing the original content to be perfectly restored. While most studies have looked at images, this paper introduces a new method for 3D mesh models, which are used in computer graphics. The authors developed a way to divide the model's vertices into layers to increase the amount of data that can be hidden without affecting the model's appearance too much. They also created a system to predict where data can be embedded more accurately, which helps maintain the quality of the 3D model. The results show that this new method is better at hiding data without causing noticeable changes to the model compared to existing techniques.
Abstract
Reversible data hiding (RDH) is a crucial information hiding technique for copyright protection and integrity verification of digital media, as it ensures the original content can be perfectly restored. However, current research on RDH remains predominantly focused on images, and relatively little attention has been paid to three-dimensional (3D) models. To address this gap, this paper proposes a RDH method for 3D mesh models based on spatial polygon prediction and dual sorting. First, a three-layer vertex division mechanism is introduced to enable a two-round embedding process, thereby expanding the number of embeddable vertices and increasing embedding capacity. Then, a spatial polygon prediction scheme is designed to balance the dual objectives of enhancing prediction accuracy and preserving embedding capacity. Furthermore, a dual sorting strategy that jointly considers angular smoothness and edge-length regularity is proposed to prioritize the smoother embedding units for data embedding, leveraging their smaller prediction errors to reduce geometric distortion. Finally, secret data is reversibly embedded into 3D mesh models using a prediction error expansion (PEE) technique guided by the dual sorting. The experimental results demonstrate that the proposed method achieves a better balance between high embedding capacity and low geometric distortion compared with other state-of-the-art spatial-domain RDH methods.
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Search related cases →Original publication on Europe PMC: https://europepmc.org/article/MED/41957064