Perception and Re-Synchronization Issues for the Watermarking of 3D Shapes
Première édition
Digital watermarking is the art of embedding secret messages in multimedia contents in order to protect their intellectual property. While the watermarking of image, audio and video is reaching maturity, the watermarking of 3D virtual objects is still a technology in its infancy.In this thesis, we focus on two main issues. The first one is the perception of the distortions caused by the watermarking process or by attacks on the surface of a 3D model. The second one concerns the development of techniques able to retrieve a watermark without the availability of the original data and after common manipulations and attacks.Since imperceptibility is a strong requirement, assessing the visual perception of the distortions that a 3D model undergoes in the watermarking pipeline is a key issue. In this thesis, we propose an image-based metric that relies on the comparison of 2D views with a Mutual Information criterion. A psychovisual experiment has validated the results of this metric for the most common watermarking attacks.The other issue this thesis deals with is the blind and robust watermarking of 3D shapes. In this context, three different watermarking schemes are proposed. These schemes differ by the classes of 3D watermarking attacks they are able to resist to. The first scheme is based on the extension of spectral decomposition to 3D models. This approach leads to robustness against imperceptible geometric deformations. The weakness of this technique is mainly related to resampling or cropping attacks. The second scheme extends the first to resampling by making use of the automatic multiscale detection of robust umbilical points. The third scheme then addresses the cropping attack by detecting robust prong feature points to locally embed a watermark in the spatial domain.
Pour plus d'informations à propos de la TVA et d'autres moyens de paiement, consultez la rubrique "Paiement & TVA".
Spécifications
- Éditeur
- Presses universitaires de Louvain
- Partie du titre
-
Numéro 113
- Auteur
- Patrice Rondao Alface,
- Collection
- Thèses de l'École polytechnique de Louvain
- Langue
- anglais
- Catégorie (éditeur)
- Sciences appliquées > Électricité
- BISAC Subject Heading
- TEC000000 TECHNOLOGY & ENGINEERING
- Code publique Onix
- 06 Professionnel et académique
- CLIL (Version 2013-2019 )
- 3069 TECHNIQUES ET SCIENCES APPLIQUEES
- Date de première publication du titre
- 01 janvier 2006
- Type d'ouvrage
- Thèse
Livre broché
- Date de publication
- 01 janvier 2006
- ISBN-13
- 9782874630378
- Ampleur
- Nombre de pages de contenu principal : 218
- Code interne
- 74552
- Format
- 16 x 24 x 1,2 cm
- Poids
- 327 grammes
- Prix
- 31,00 €
- ONIX XML
- Version 2.1, Version 3
Google Livres Aperçu
Publier un commentaire sur cet ouvrage
Sommaire
List of figures ixI General Introduction and Notions of Digital Shape Processing11 General Introduction 31.1 Context of the work . . . . . . . . . . . . . . . . . . . . . . . . 31.2 Organization of the thesis . . . . . . . . . . . . . . . . . . . . 62 Digital Shape Processing 92.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.2 Shapes and their digital representations . . . . . . . . . . . . 92.2.1 Non-Uniform Rational B-Splines (NURBS) . . . . . . 102.2.2 3D triangle meshes . . . . . . . . . . . . . . . . . . . . 112.3 Digital Topology . . . . . . . . . . . . . . . . . . . . . . . . . . 132.4 Parameterization . . . . . . . . . . . . . . . . . . . . . . . . . 142.4.1 Digital parameterization . . . . . . . . . . . . . . . . . 162.4.2 Distortion energy estimation . . . . . . . . . . . . . . 162.4.3 Tutte parameterization . . . . . . . . . . . . . . . . . . 182.4.4 Conformal and harmonic parameterizations . . . . . 182.4.5 Authalic parameterization . . . . . . . . . . . . . . . . 192.5 Differential Geometry . . . . . . . . . . . . . . . . . . . . . . 202.6 Geodesic distances . . . . . . . . . . . . . . . . . . . . . . . . 252.7 Spectral Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 262.8 Multiresolution Analysis . . . . . . . . . . . . . . . . . . . . . 272.9 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30iv CONTENTSII Shape Perception 313 Perception of a 3D Shape 333.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333.2 Visualization Techniques . . . . . . . . . . . . . . . . . . . . . 343.2.1 Rendering . . . . . . . . . . . . . . . . . . . . . . . . . 343.2.2 Lighting Models . . . . . . . . . . . . . . . . . . . . . 353.2.3 Textures . . . . . . . . . . . . . . . . . . . . . . . . . . 393.2.4 Global Illumination . . . . . . . . . . . . . . . . . . . . 403.2.5 Non-Photorealistic Rendering (NPR) . . . . . . . . . . 413.2.6 Level of Detail . . . . . . . . . . . . . . . . . . . . . . . 423.3 Three-Dimensional Distance Metrics . . . . . . . . . . . . . . 433.3.1 Hausdorff distance . . . . . . . . . . . . . . . . . . . . 443.3.2 Volume based metric . . . . . . . . . . . . . . . . . . . 443.3.3 Quadric Error Measure . . . . . . . . . . . . . . . . . 453.3.4 Curvature Based Distance . . . . . . . . . . . . . . . . 453.3.5 RMSE, VSNR and Geometric Laplacian . . . . . . . . 453.3.6 Image-based metrics . . . . . . . . . . . . . . . . . . . 463.3.7 Perceptive models . . . . . . . . . . . . . . . . . . . . 463.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 484 Shape Quality Assessment for 3D Watermarking Attacks 494.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 494.2 Visual similarity metric through 2D projections . . . . . . . . 504.2.1 Mutual information . . . . . . . . . . . . . . . . . . . 504.2.2 Generating 2D views . . . . . . . . . . . . . . . . . . . 514.3 Subjective evaluation experiment . . . . . . . . . . . . . . . . 534.3.1 Experiment script . . . . . . . . . . . . . . . . . . . . . 554.3.2 Statistical Analysis of the Experimental Results . . . 564.4 Validation of the proposed metric . . . . . . . . . . . . . . . . 604.4.1 Noise addition . . . . . . . . . . . . . . . . . . . . . . 614.4.2 Vertex simplification . . . . . . . . . . . . . . . . . . . 624.4.3 Smoothing . . . . . . . . . . . . . . . . . . . . . . . . . 624.4.4 Image-based metrics and view variance . . . . . . . . 634.4.5 Final observations . . . . . . . . . . . . . . . . . . . . 644.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65CONTENTS vIII Towards 3D ShapeWatermarking 695 Introduction to Digital Watermarking 715.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 715.2 Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . 725.3 Applications of watermarking . . . . . . . . . . . . . . . . . . 755.3.1 Copy protection . . . . . . . . . . . . . . . . . . . . . . 755.3.2 Copyright protection . . . . . . . . . . . . . . . . . . . 765.3.3 Integrity protection . . . . . . . . . . . . . . . . . . . . 765.3.4 Self-indexed contents . . . . . . . . . . . . . . . . . . . 765.3.5 Augmented contents . . . . . . . . . . . . . . . . . . . 775.3.6 Steganography . . . . . . . . . . . . . . . . . . . . . . 775.3.7 Fingerprinting . . . . . . . . . . . . . . . . . . . . . . . 775.4 Watermarking of 3D Data . . . . . . . . . . . . . . . . . . . . 785.4.1 Why should 3D graphics content be protected? . . . . 785.4.2 Are there alternatives to 3D watermarking? . . . . . . 795.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 826 Survey of 3D DigitalWatermarking: From 3D Mesh Data Hidingtowards 3D Shape Blind and Robust Watermarking 836.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 846.2 3D Watermarking Applications and Requirements . . . . . . 856.2.1 3D Watermarking Applications . . . . . . . . . . . . . 856.2.2 Robustness and Attacks . . . . . . . . . . . . . . . . . 856.2.3 Imperceptibility . . . . . . . . . . . . . . . . . . . . . . 876.2.4 Capacity, Content and Security . . . . . . . . . . . . . 896.2.5 3D Watermarking Schemes Requirements . . . . . . . 906.3 3D Watermarking Schemes . . . . . . . . . . . . . . . . . . . 916.3.1 Spatial Domain . . . . . . . . . . . . . . . . . . . . . . 916.3.2 Transform Domain . . . . . . . . . . . . . . . . . . . . 1006.3.3 Compression Domain . . . . . . . . . . . . . . . . . . 1056.3.4 Attribute Domain . . . . . . . . . . . . . . . . . . . . . 1056.3.5 3D embedding and 2D retrieval . . . . . . . . . . . . . 1066.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1067 Spectral Watermarking of 3D meshes 1097.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1097.2 Spectral decomposition . . . . . . . . . . . . . . . . . . . . . . 1107.2.1 Discrete Laplacian Operator . . . . . . . . . . . . . . . 110vi CONTENTS7.2.2 Discrete Laplacian operators and the Principal Component Analysis . . . . . . . . . . . . . . . . . . . . . . 1127.2.3 Discrete Laplacian operators as connectivity-preserving linear mesh system . . . . . . . . . . . . . . . . . . . . 1137.2.4 Properties of discrete combinatorial mesh Laplacian operators . . . . . . . . . . . . . . . . . . . . . . . . . . 1157.2.5 Mesh spectra . . . . . . . . . . . . . . . . . . . . . . . 1177.3 Fixed basis decomposition, partitioning and overlapping . . 1187.3.1 Tutte projection . . . . . . . . . . . . . . . . . . . . . . 1207.3.2 Vertex mapping . . . . . . . . . . . . . . . . . . . . . . 1207.3.3 Partitioning and patch augmentation by overlapping 1227.4 Mesh geometry compression and progressive transmission . 1247.4.1 Visual metric . . . . . . . . . . . . . . . . . . . . . . . 1257.4.2 Geometric compression . . . . . . . . . . . . . . . . . 1267.4.3 Progressive transmission . . . . . . . . . . . . . . . . 1277.5 Watermarking . . . . . . . . . . . . . . . . . . . . . . . . . . . 1287.5.1 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . 1287.5.2 Results and discussion . . . . . . . . . . . . . . . . . . 1307.6 Conclusion, limitations and future work . . . . . . . . . . . . 1348 Re-Synchronization of Spectral Watermarks by Umbilical Points1378.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1378.2 Feature Points . . . . . . . . . . . . . . . . . . . . . . . . . . . 1388.2.1 Umbilical Points . . . . . . . . . . . . . . . . . . . . . 1398.2.2 Curvature Tensor Estimation . . . . . . . . . . . . . . 1408.2.3 Curvature Tensor Field Filtering and Topological Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 1418.2.4 Multi-Scale Analysis and Robust Umbilical Points . . 1508.2.5 Analogy with the Scale-Invariant Feature Transform 1518.3 Partition And Remeshing . . . . . . . . . . . . . . . . . . . . 1528.3.1 Sampling Independent Wavefronts . . . . . . . . . . . 1528.3.2 Building The Geodesic Delaunay Triangulation . . . 1538.3.3 Remeshing . . . . . . . . . . . . . . . . . . . . . . . . . 1548.4 Watermarking Encoding And Decoding . . . . . . . . . . . . 1558.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1568.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1599 3D Blind Local Watermarking and Robustness to Resampling and Cropping Attacks 1619.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161CONTENTS vii9.2 Overview of the proposed scheme . . . . . . . . . . . . . . . 1639.3 Robust Prongs Detection . . . . . . . . . . . . . . . . . . . . . 1659.3.1 Protrusion Function . . . . . . . . . . . . . . . . . . . 1659.3.2 Geodesics . . . . . . . . . . . . . . . . . . . . . . . . . 1669.3.3 Detecting Local Maxima . . . . . . . . . . . . . . . . . 1679.3.4 Computational Cost Optimization . . . . . . . . . . . 1699.4 Robust Local Neighborhoods . . . . . . . . . . . . . . . . . . 1709.5 Patch Radial Watermarking . . . . . . . . . . . . . . . . . . . 1729.5.1 Robust Center of Gravity Estimation . . . . . . . . . . 1739.5.2 Shape Histogram . . . . . . . . . . . . . . . . . . . . . 1749.5.3 Watermark Embedding . . . . . . . . . . . . . . . . . 1769.5.4 Watermark Decoding . . . . . . . . . . . . . . . . . . . 1789.6 Robustness Experimental Results . . . . . . . . . . . . . . . . 1799.6.1 Robustness of Prong Detection . . . . . . . . . . . . . 1799.6.2 Robustness of the Watermarking Scheme . . . . . . . 1799.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18010 Conclusion 18310.1 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . 18310.1.1 Perceptual metric . . . . . . . . . . . . . . . . . . . . . 18410.1.2 Blind semi-fragile watermarking scheme based on spectral decomposition . . . . . . . . . . . . . . . . . 18410.1.3 Blind watermarking scheme robust against resampling18510.1.4 Blind watermarking scheme robust against resampling and cropping . . . . . . . . . . . . . . . . . . . . 18510.1.5 Related Applications . . . . . . . . . . . . . . . . . . . 18610.2 Lines of Future Work . . . . . . . . . . . . . . . . . . . . . . . 186Bibliography 189