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Robust Watermarking Approach for 3D Triangular

Mesh using Self Organization Map

By

Mona M.Soliman

Cairo university

Scientific Research Group in Egypt (SRGE)

2013 8th International Conference on Computer Engineering & Systems (ICCES) 26 Nov - 28 Nov

2013

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LOGO Scientific Research Group in Egypt

www.egyptscience.net

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LOGO Agenda

2. Introduction

3. Objective and problem definition

4. Background

5. Proposed 3D watermarking scheme

6. Experimental Results

7. Conclusions.

1. Motivations

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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.

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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.

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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.

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4.1 Self Organization Map

4.2 3D Mesh Basics

4.3 Vertex Smoothness Measure

Background

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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.

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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 .

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• 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 .

3D mesh Basics

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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.

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LOGO Agenda

2. Introduction

3. Objective and problem definition

4. Background

5. Proposed 3D watermarking scheme

6. Experimental results

7. Conclusions.

1. Motivations

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LOGO Proposed 3D watermarking scheme

4.1 Vertex Clustering Based on Self Organization Map

4.2 Watermark Insertion Procedure

4.3 Watermark Extraction Procedure

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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)

Vertex Clustering Based on SOM

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Framework of the Proposed approaches for 3D mesh watermarking

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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.

In the insertion procedure we use two insertion methods:

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LOGO Watermark Extraction Procedure

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

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

both approaches such that:

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LOGO Agenda

2. Introduction

3. Objective and problem definition

4. Background

5. Proposed 3D watermarking scheme

6. Experimental Results

7. Conclusions.

1. Motivations

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Experimental Results Data set description

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LOGO Experimental Results Distortion evaluation

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LOGO Experimental Results Distortion evaluation

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LOGO Experimental Results Robustness evaluation

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LOGO Experimental Results Robustness evaluation

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LOGO Experimental Results

Robustness evaluation

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LOGO Experimental Results

Robustness evaluation

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LOGO Experimental Results

Robustness evaluation

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LOGO Experimental Results

Robustness evaluation

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LOGO Experimental Results

Robustness evaluation

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LOGO Robustness response

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LOGO Robustness response

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LOGO Robustness response

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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.

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Thank you E-mail: mona.solyman@fci-cu.edu.eg