maximizing strength of digital watermarks using neural networks

cracklegulleyAI and Robotics

Oct 19, 2013 (3 years and 10 months ago)

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Maximizing Strength of Digital
Watermarks Using Neural
Network

Presented by Bin
-
Cheng Tzeng

5/21 2002

Kenneth J.Davis; Kayvan Najarian


International Conference on Neural Networks,
2001. Proceedings.

Outlines


Introduction


A Watermarking Technique in the
DWT Domain


Neural Technique for Maximum
Watermark


Conclusions

Introduction


For watermarking to be successful

1.Unobtrusive


2.robust


In other words, one would like to
insert the watermark with maximum
strength before it becomes visible to
the human visual system(HVS)

Introduction(Cont.)


The way the strength of the added
watermark is chosen is of highest
importance.


This paper attempts to define a neural
network based algorithm to
automatically control and select the
watermarking parameters to create
maximum
-
strength watermarks.

A Watermarking Technique in
the DWT Domain


The paper use a wavelet
-
based scheme
for digital watermarking.

(reference

A New Wavelet
-
Based
Scheme for Watermarking Images

)



The technique was tested by cropping,
JPEG compression, Gaussian noise,
halfsizing, and median filtering.

A Watermarking Technique in
the DWT Domain

A Watermarking Technique in
the DWT Domain


A threshold was used to determine the
significant coefficients.




The watermark is added to the
significant coefficients of all the bands
other than the low pass subband.

A Watermarking Technique in
the DWT Domain



: The scaling parameter

c
i

: The coefficient of the original image

m
i
: The watermark to be added

c
i


: the watermarked coefficient


Neural Technique for
Maximum Watermark


To achieve maximal watermarking while
remaining invisible to the human eye.

1.Generating a watermarked image

using a given power

2.allowing one or more persons to

judge the image,repeat while

increasing the power until the

humans deem the watermark visible

Neural Technique for
Maximum Watermark


Replacing the humans in the process
with a neural network allowing the
process to be automated.


To train the neural network, a database
of original and watermarked images
whose qualities are judged by several
human subjects is being created.

Neural Technique for
Maximum Watermark


When judging the images, a score is
given between 0 and 100


0 means no perceivable difference
between the original image and
watermarked image and 100 means the
watermark has highly distorted the
image.

Neural Technique for
Maximum Watermark


Feed forward back
-
propagation network


Being able to properly approximate
non
-
linear functions and if properly
trained will perform reasonably well
when presented with inputs it has not
seen before


HVS is non
-
linear


To be useful.

Neural Technique for
Maximum Watermark

Neural Technique for
Maximum Watermark


Each image is subdivided into blocks of
64x64 pixels to be treated as a
complete image.


4096 inputs and 1 final input (

)


The hidden layer with 256 or 512
neurons

Neural Technique for
Maximum Watermark


The network is trained using the scaled
conjugate gradient algorithm(SCG)



Trained for 300
-
600 iterations or until
the mean square error is less than
0.00001

Comparison of Neural Network and
Human watermark visibility scores

Conclusions


The watermark is added to both low
and high scales of DWT.


To aid in maximizing the watermark a
neural network that mimics the HVS
was proposed.


When properly trained, the neural
network can allow it to be used in place
of several human reviewers.