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3 d wm monasolyman_10nov_ainshames
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
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2013 8th International Conference on Computer Engineering & Systems (ICCES) 26 Nov - 28 Nov
2013
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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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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.
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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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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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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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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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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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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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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)
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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.
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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
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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.
