1. University ofTUNISIA:ESSTT
CEREP Research Unit
hassene.seddik@esstt.rnu.tn
Shaping optimal parameters selection for
most favourable robustness and
imperceptibility in watermarking in the
DWT domain
1
2. TABLE
INTRODUCTION
WATERMARKING METHODS
WATERMARKING TECHNIQUES IN DWT DOMAINS
OPTIMIZATIONS
Optimal wavelet choice.
Optimal frequency sub-band choice
Selection of the optimal embedding force “ α”
choice of the most useful embedding equation
Selection of the optimal decomposition level
CONCLUSION
2
3. Widespread of numeric data exchanges
Increase of commercial activity on Internet and media industries
NEED
copyright protection and data owner identification.
protection of media such as images, video and audio against illicit
processing and use.
to resolve these problems
DIGITAL WATERMARKING
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5. WATERMARKING TECHNIQUES IN DWT
DOMAINS
5
countless papers proposing different algorithms :
Hiding binary logos hierarchically decomposed in DWT and added
to the image sub-bands.
Binary watermark coded in selected coefficients of the detail bands
Embedding a watermark in the low frequency sub-band
Using the LSB technique of DWT sub-band coefficients, the
selected modified bits are chosen with respect to the HSV….
6. Parameters managing the
watermarking scheme
6
Kind of wavelet to be used
Frequency band used to code the watermark
Power of embedding with respect to HSV
Embedding techniques
Level of sub-bands decomposition
7. Experimental configuration
7
The original image is transformed in the time-frequency domain,
the watermark is coded. After recovering the spatial
representation of the watermarked image many STIRMARK
attacks are applied in order to test the robustness of the
embedding algorithm. These tests allow as checking and finding
the equilibrium point between the involved parameters.
In every set of tests one parameter is varied, and the others are
fixed and the optimal value is determined based on the distortions
measurements.
9. Optimal wavelet choice
9
wavelet can generate loss or lossless decomposition.
Varying sub-bands coefficients provoke distortions in the spatial
representation depending on the wavelet kind.
Check witch wavelet type can affect the less the spatial image if
the watermark is added.
The diagonal sub-band is used
Embedding power is fixed as 0.8
The wavelet that engender less distortion
to the spatial representation of the
watermarked image is considered.
10. Optimal frequency sub-band
choice
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In witch sub-band is
better to code the
watermark ?
Fixing the parameters that have not been optimized
Insert the same watermark with the same embedding gain
factor in the different iteratively frequency sub-band.
The insertion that engenders lower distortions
to the original image is considered
11. 11
( )( ).1.,,0,,0,,0,,0 jiN
LH
ji
LH
ji
LH
ji
LH
ji
WXXXY meanmean +
+−+= α
( )( )jiNMN
HL
ji
HL
ji
HL
ji
HL
ji WXXXY meanmean ++++= .1.- ,,0,,0,,0,,0 α
( )( )jiNMN
HH
ji
HH
ji
HH
ji
HH
ji
WXXXY meanmean ++
+−+= 2,,0,,0,,0,,0
.1. α
Gain factor
coefficient
PSNR (HL1 sub-
band insertion)
PSNR (LH1 sub-
band insertion)
PSNR (HH1 sub-band
insertion)
0.1 50.49 48.11 55.18
0.2 44.47 42.09 49.27
0.3 40.95 38.57 4581
0.4 38.45 36.07 43.29
0.5 36.51 34.13 41.36
0.6 34.95 32.55 39.82
0.7 33.39 31.21 38.50
0.8 32.43 30.05 37.41
0.9 31.41 29.03 36.43
α
HH sub-band presents more reliability with respect to the HSV
and causes less distortion to the processed image
12. Selection of the optimal embedding
force “ α”
12
The α called gain factor is the first parameter in charge of the
robustness of any watermarking algorithm.
α is thresholded by the HSV imperceptibility limit.
A limit of 37dB is fixed to decide about the presence of visible distortions
The optimal gain factor
corresponding to the fixed
PSNR threshold is 0.8 .
13. choice of the most useful
embedding equation
13
Different equations are proposed in the literatures
).1).(),((),( kmeanmeanw
wfjiffjif α+−+=
).1.(),(),( kw
wjifjif α+=
kw wjifjif .),(),( α+=
The second equation is
more reliable and improves
the robustness of the
algorithm compared with
the first one.
14. Choice of the optimal
decomposition level
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the first decomposition generates sub-bands (LH, HL, and HH).
The LL sub-band is re-decomposed to generate the next level of
decomposition .
For an n-level decomposition and M×N image, the size of the
area in which watermarks are to be embedded is : M.N/22n
n
MN
2
2 n
MN
2
2 n
MN
2
2
Third level decomposition of
LENA image.
15. 15
Different level watermarked sub-bands and the differences between
the watermarked and original images.
• we deduce that more the decomposition level is high, more the
watermark is spread and distributed over and near the borders.
• This distribution is controlled in the frequency domain
depending on the non-randomly selected sub-bands coefficients.
17. CONCLUSIONCONCLUSION
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An overview over different watermarking techniques in the
DWT domain is presented.
A strategy to optimize the different parameters that intervene
in the watermarking process is built up.
Experiments and tests are conducted to find the optimal value
that leads to a robust and imperceptible watermarking
algorithm. .
Injecting these optimal parameters in the embedding equation
of the watermarking process, guarantee better robustness of the
watermarked image against different attacks and decreases the
distortions to maintain them under the perceptibility threshold.
This optimization wasn’t done in the literature and can be
exploited easily for DWT watermarking shames.
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THANK YOU FOR YOUR
ATTENTION
Seddik_hassene@yahoo.fr hassene.seddik@esstt.rnu.tn
