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Abstract
ABSTRACT
Intelligent 3D Watermarking System and Applications
By
Mourad Raafat Mouhamed
Mathematics Department, Faculty of Science, Helwan
University
The aim of this work was to propose an intelligent 3D watermarking system.
Ultimately, a good watermark should satisfy some watermarking requirements,
such as imperceptibility and robustness against attacks. The 3D watermarking
techniques classified into three generation. The third-generation techniques
add intelligent layer to the techniques wish used in the first and second
generation. This intelligent layer help to solve the trade of between the two
requirements of any watermarking system. In this work we proposed three
watermarking approaches first one is novel robust and blind 3D watermarking
approach that used the spherical coordinates of the vertices of the 3D mesh
model to embed the watermark using a random table as a key to makes it more
secure. Experimental results demonstrate that the proposed approach is giving
pretty immeasurable imperceptibility compared with other techniques likewise
is robust against different types of attacks, for example, Similarity
transformation, cropping, subdivision, and simplification. Secondly, an
intelligent phase added to this approach where the second approach presents a
robust 3D mesh watermarking approach, that adopts an optimization method
of selecting vertices from 3D mesh models, that will carry the watermark
bitstream. The proposed watermark approach depends on an embedding
algorithm that use a clustering method in combination with swarm algorithm
to divide the mesh model vertices into groups. A Points of interest set (POIs)
are selected from these groups and mark it as watermark carriable vertices.
This proposed approach inserts the watermark bitstream in the decimal part of
spherical coordinates for these selected vertices. The experimental results
confirm that the proposed optimization approach of watermark position
Abstract
selection and embedding proves its superiority compared with other techniques
with respect to imperceptibility and robustness. On the other hand, we
proposed another Intelligent approach that embedding the watermark bitstream
using the Coyote optimization algorithm. The approach starts by selecting the
best vertices that will carry the watermark bits using k-means clustering
method. Followed by watermark embedding step using COA in finding the
best local statistical measure modification value. Finally, we extract the
embedded watermark without any need of the original model. The proposed
approach is validated using different visual fidelity and robustness measures.
The experimental results of the proposed approach will be compared with other
state of the art approaches to prove its superiority in embedding and extraction
of watermark bits sequence with respect to both robustness and
impracticability.
Keywords: Three-dimensions model, Watermark, Machine learning, Particle
Swarm optimization, Coyote optimization algorithm, Spherical coordinates,
Statistical distribution.