Cropping-resilient 3D mesh watermarking based on consistent segmentation and mesh steganalysis

This paper presents a new approach to 3D mesh watermarking using consistent segmentation and mesh steganalysis. The method is blind, statistical, and highly robust to cropping attack. The primary watermarking domain is calculated by shape diameter function and the outliers of segments are eliminated...

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Bibliographic Details
Published in:Multimedia tools and applications 2018-03, Vol.77 (5), p.5685-5712
Main Authors: Jang, Han-Ul, Choi, Hak-Yeol, Son, Jeongho, Kim, Dongkyu, Hou, Jong-Uk, Choi, Sunghee, Lee, Heung-Kyu
Format: Article
Language:English
Subjects:
3-D printers
Algorithms
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Editing
Methods
Multimedia Information Systems
Norms
Outliers (statistics)
Registration
Resists
Segmentation
Segments
Signal processing
Special Purpose and Application-Based Systems
Watermarking
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Summary:This paper presents a new approach to 3D mesh watermarking using consistent segmentation and mesh steganalysis. The method is blind, statistical, and highly robust to cropping attack. The primary watermarking domain is calculated by shape diameter function and the outliers of segments are eliminated by computing the consistency interval of vertex norms. In the watermark embedding process, the mesh is divided into several segments and the same watermark is inserted in each segment. In the watermark extraction process, the final watermark among watermark candidates extracted from multiple segments is determined through watermark trace analysis that is kind of mesh steganalysis. We analyze the watermark trace energy of multiple segments of a mesh and detect the final watermark in the segment with the highest watermark trace energy. To analyze the watermark trace energy, we employ nonlinear least-squares fitting. The experimental results show that the proposed method not only achieves significantly high robustness against cropping attack, but also resists common signal processing attacks such as additive noise, quantization, smoothing and simplification.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-017-4483-6