Scientific Research Group in Egypt (SRGE)

Robust Watermarking Approach for 3D Triangular
Mesh using Self Organization Map...
Company

LOGO

Scientific Research Group in Egypt
www.egyptscience.net
Company

LOGO

Agenda
1. Motivations
2. Introduction
3. Objective and problem definition

4. Background
5. Proposed 3D wat...
Company

LOGO

Motivation

 It is essential to provide a robust technique for copyright protection and/or content
authent...
Company

LOGO





Introduction

Watermark robustness is the
ability to recover the watermark
even if the watermarked 3D...
Company

LOGO


Objective

The objective of this paper is to explore innovative
ways to insert the maximum amount of secr...
Company

LOGO

Background

4.1 Self Organization Map
4.2 3D Mesh Basics
4.3 Vertex Smoothness Measure
Company

LOGO

Self Organizing Map

 Self-Organizing means no supervision
is required. SOMs learn on their own
through
un...
Company

LOGO

3D mesh Basics

•

Mathematically, a 3D mesh containing N
vertices and M edges can be modelled as a
signal ...
Company

LOGO

3D mesh Basics

•

Mathematically, a 3D mesh
containing N vertices and M edges
can be modelled as a signal ...
Company

LOGO

Vertex Smoothness Measure

 The smoothness feature measure
the angle variation between
surface normal and ...
Company

LOGO

Agenda
1. Motivations
2. Introduction
3. Objective and problem definition

4. Background
5. Proposed 3D wat...
Company

LOGO

Proposed 3D watermarking scheme

4.1 Vertex Clustering Based on Self Organization Map
4.2 Watermark Inserti...
Company

LOGO

Vertex Clustering Based on SOM

 This performed by using SOM
to cluster the whole mesh
vertices into three...
Company

LOGO

Watermark Insertion Procedure

Framework of the Proposed approaches for 3D mesh watermarking
Company

LOGO

Watermark Insertion Procedure

In the insertion procedure we use two insertion methods:
 The first method ...
Company

LOGO

Watermark Extraction Procedure

Once the locations of watermark bits are located ,WM bits are extracted for...
Company

LOGO

Agenda
1. Motivations
2. Introduction
3. Objective and problem definition

4. Background
5. Proposed 3D wat...
Company

LOGO

Experimental Results
Data set description
Company

LOGO

Experimental Results
Distortion evaluation
Company

LOGO

Experimental Results
Distortion evaluation
Company

LOGO

Experimental Results
Robustness evaluation
Company

LOGO

Experimental Results
Robustness evaluation
Company

LOGO

Experimental Results
Robustness evaluation
Company

LOGO

Experimental Results
Robustness evaluation
Company

LOGO

Experimental Results
Robustness evaluation
Company

LOGO

Experimental Results
Robustness evaluation
Company

LOGO

Experimental Results
Robustness evaluation
Company

LOGO

Robustness response
Company

LOGO

Robustness response
Company

LOGO

Robustness response
Company

LOGO

Conclusions
 This work, provides a novel watermarking algorithm in which
vertices are selected from the 3D...
Company

LOGO

Thank you
E-mail: mona.solyman@fci-cu.edu.eg
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3 d wm monasolyman_10nov_ainshames

  1. 1. Scientific Research Group in Egypt (SRGE) Robust Watermarking Approach for 3D Triangular Mesh using Self Organization Map By Mona M.Soliman Cairo university Company LOGO 2013 8th International Conference on Computer Engineering & Systems (ICCES) 26 Nov - 28 Nov 2013
  2. 2. Company LOGO Scientific Research Group in Egypt www.egyptscience.net
  3. 3. Company LOGO Agenda 1. Motivations 2. Introduction 3. Objective and problem definition 4. Background 5. Proposed 3D watermarking scheme 6. Experimental Results 7. Conclusions.
  4. 4. Company LOGO Motivation  It is essential to provide a robust technique for copyright protection and/or content authentication of graphics data in a universal multimedia access framework.  One approach to meet this requirement is the use of digital watermarking.  Since 3-D mesh watermarking techniques were introduced, there have been several attempts to improve the performance in terms of transparency and robustness.
  5. 5. Company LOGO   Introduction Watermark robustness is the ability to recover the watermark even if the watermarked 3D model has been manipulated. Usually, one hopes to construct a robust watermark which is able to go through common malicious attacks for copyright protection purposes. There are two kinds of 3D mesh watermarking algorithms: Spatial domain based algorithm. Transformation domain based algorithm.
  6. 6. Company LOGO  Objective The objective of this paper is to explore innovative ways to insert the maximum amount of secret information into 3D mesh models without causing perceptual distortion and also make it difficult for the attacker to guess where the watermark was inserted.  Watermark insertion is performed on specific set of vertices that are selected by utilizing Self Organization Maps (SOM) . SOM is a kind of competitive neural network in which the networks learn to form their own classifications.  Two methods were used to embed the watermark into 3D model.  (1) statistical approach that modified the distribution of vertex norms to hide watermark information into host 3D model  (2) mixed insertion of watermark bits into host model using vertex norm distribution and mesh vertices at the same time.
  7. 7. Company LOGO Background 4.1 Self Organization Map 4.2 3D Mesh Basics 4.3 Vertex Smoothness Measure
  8. 8. Company LOGO Self Organizing Map  Self-Organizing means no supervision is required. SOMs learn on their own through unsupervised competitive learning.  In competitive learning, the elements of the network compete with each other for the right to provide the output associated with an input vector.  Only one element is allowed to answer the query and this element simultaneously inhibits all other competitors.
  9. 9. Company LOGO 3D mesh Basics • Mathematically, a 3D mesh containing N vertices and M edges can be modelled as a signal M = G,C. • The set of all the neighbours of a vertex vi is called 1-ring of the vertex. • The number of neighbours of vi in it’s 1-ring neighborhood is the valence or degree of the vertex vi .
  10. 10. Company LOGO 3D mesh Basics • Mathematically, a 3D mesh containing N vertices and M edges can be modelled as a signal M = G,C. • The set of all the neighbours of a vertex vi is called 1-ring of the vertex. • The number of neighbours of vi in it’s 1-ring neighborhood is the valence or degree of the vertex vi .
  11. 11. Company LOGO Vertex Smoothness Measure  The smoothness feature measure the angle variation between surface normal and the average normal corresponding to a vertex.  smoothness measure reflect the local geometry of a surface or region.  Both flat and peak regions can’t be used to hide watermark bits. We have to neglect these regions and mark their vertices as unsuitable watermark carrier.
  12. 12. Company LOGO Agenda 1. Motivations 2. Introduction 3. Objective and problem definition 4. Background 5. Proposed 3D watermarking scheme 6. Experimental results 7. Conclusions.
  13. 13. Company LOGO Proposed 3D watermarking scheme 4.1 Vertex Clustering Based on Self Organization Map 4.2 Watermark Insertion Procedure 4.3 Watermark Extraction Procedure
  14. 14. Company LOGO Vertex Clustering Based on SOM  This performed by using SOM to cluster the whole mesh vertices into three clusters (large, medium, and low), label the medium clusters’ vertices to be suitable watermark carriers.  We train four SOM neural networks, each of which is trained by different feature vectors of length (4,5,6,7)
  15. 15. Company LOGO Watermark Insertion Procedure Framework of the Proposed approaches for 3D mesh watermarking
  16. 16. Company LOGO Watermark Insertion Procedure In the insertion procedure we use two insertion methods:  The first method is based on embedding watermark into the 3-D mesh model by modifying the distribution of vertex norms . The distribution is divided into distinct sections, referred to as bins, each of which is used as a watermark embedding unit to embed one bit of watermark.  The second method is based on a new idea of partitioning the watermark bits into two part, The first part is inserted on the norm distributions as illustrated before while the second part is inserted directly in the vertices.
  17. 17. Company LOGO Watermark Extraction Procedure Once the locations of watermark bits are located ,WM bits are extracted for both approaches such that:  The proposed approach I using the first method of insertion is considered a semi-blind watermarking procedure All we need is the trained SOM to detect the locations of watermark insertion.  For proposed approach II we need the original mesh model at extraction phase, So it is considered as non-blind watermarking procedure
  18. 18. Company LOGO Agenda 1. Motivations 2. Introduction 3. Objective and problem definition 4. Background 5. Proposed 3D watermarking scheme 6. Experimental Results 7. Conclusions.
  19. 19. Company LOGO Experimental Results Data set description
  20. 20. Company LOGO Experimental Results Distortion evaluation
  21. 21. Company LOGO Experimental Results Distortion evaluation
  22. 22. Company LOGO Experimental Results Robustness evaluation
  23. 23. Company LOGO Experimental Results Robustness evaluation
  24. 24. Company LOGO Experimental Results Robustness evaluation
  25. 25. Company LOGO Experimental Results Robustness evaluation
  26. 26. Company LOGO Experimental Results Robustness evaluation
  27. 27. Company LOGO Experimental Results Robustness evaluation
  28. 28. Company LOGO Experimental Results Robustness evaluation
  29. 29. Company LOGO Robustness response
  30. 30. Company LOGO Robustness response
  31. 31. Company LOGO Robustness response
  32. 32. Company LOGO Conclusions  This work, provides a novel watermarking algorithm in which vertices are selected from the 3D model for watermarking by using SOM neural networks without causing perceptible distortion.  We use statistical watermarking methods for 3D mesh models that modify the distribution of vertex norms via changing the mean of each bin.  To enhance both transparency and robustness we introduce two novel approaches that insert watermark bits based on intelligence vertex selection.  Proposed approach I provides good results in terms of imperceptibility while proposed approach II provides better results in terms of robustness and at the same time it maintain accepted results of imperceptibility.
  33. 33. Company LOGO Thank you E-mail: mona.solyman@fci-cu.edu.eg

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