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Protection of digital watermarking
based on SVD against false positive
detection vulnerability
UNIVERSITY HADJ LAKHDER -BATNA-
SCIENCES FACULTY
COMPUTER SCIENCE DEPARTEMENT
By: Belferdi Wassila
Dr Behloul Ali
INTERNATIONAL CONFERENCE ON
ADVANCED COMMUNICATION AND
INFORMATION SYSTEMS
Plan
• Introduction:
• Singular Value Décomposition:
• False Positive Détection Vulnerability:
• Proposed Method:
• Sharing Secret Principle:
• Experiment Results :
• Robustness Conditions Of The Proposed Method:
• Conclusion and perspectives:
2
Introduction
• Day by day, the digital watermarking is becoming a
promising technique to protect digital data.
• It has seen numerous novel article covering new
techniques; each one of those techniques have there
advantages and inconveniences.
• In recent years, the techniques using linear algebra
has attracted attention of researchers to using it for
watermarking(e.g. SVD).
3
• From the viewpoint of linear algebra we can observe that
a discrete image is an array of non-negative scalar entries
which may be regarded as a matrix.
M=USV’=S li UiVi
4
Singular Value Décomposition (1)
M U S V’
m
n
=
m r
n
5
SVD Based Watermarking Example
Watermark
Hôst image
Uw
Sw
Vw
U
S
V
Insertion of
watermark in
hôst image
Watermarked
Image
False Positive Détection Vulnerability
• Duo to the watermark insertion method, another
watermark rather than the original can be reconstructed as
the embedded watermark; causing the false positive
detection vulnerability .
• If an attacker use U* and V* matrices of his own
watermark in place of reserved ones he can show his own
watermark.
6
False Positive Détection Vulnerability (2)
7
Proposed Method
• To avoid that an attacker reconstruct his watermark
using their own matrices U* and V*; the idea is to share
a secret key D between matrices Uw, Sw and Vw of
watermark.
• During the embedding phase, for each matrices Uw, Sw
and Vw of watermark a key is inserted, then use the
modified S* to reconstruct the watermarked image,
those keys are calculated using sharing secret principle.
8
Sharing Secret Principle
• The goal is to divide a secret key D into pieces.
• The coefficients a1... ak-1 are randomly chosen from a uniform
distribution over the integers in [0, p-1]
• Pick a random k-1 degree polynomial q(x)=a0+alx+ . . . ak-1xk-1
in which a0=D.
• The values D1,..., Dn are evaluate:
D1= q(1) ,..., Di = q(i) ,..., Dn = q(n).
• Given any subset of k of these Di, we can find the
coefficients of q(x) by interpolation, and evaluate D=q (0).
9
Embedding Phase
10
Extraction Phase
11
Experiment Results
12
Robustness results of the proposed method against attacks
Attacks We W
*e |D-Dc| |D-D*c|
Cropping 5 188037
Gaussian
noise 5 1888037
Rotation
0,2° 46568 234611
Resizing
44095 232139
Contrast
adjustment 425 188469
13
Experiment Results(2)
the size
of
image
We W*e |D-Dc| |D-D*c|
512×512 5 188037
256×256 36 188007
128×128 913 187131
64×64 2406 185638
Results of the influence of image size on the robustness of the proposed method
Robustness Conditions Of The Proposed
Method
14
• In the SVD, the biggest
part of energy is
concentrated in low
frequencies of images,
the watermarking profit
this property to insert
watermark in low and
middle frequencies
according to
robustness/invisibility
compromiser
influence of the inserted secret on the quality of the watermarked image
Conclusion
• The novelty of our scheme is the use of the sharing secret
principle in watermark embedding, that is adaptively chosen
according to the local features of the image.
• The aim of our solution is to hide the watermark and insure
their robustness against the false positive detection
vulnerability.
• The experimental results obtained give evidence that our
scheme is robust against several attacks.
15
Perspectives
Our perspective turns around:
• increasing the robustness of our solution using block based
SVD scheme.
• Conceive a méthode to chose coefficients a1... ak-1 randomly to
obtain better results.
16
QUESTIONS ?
17

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SVD-based digital watermarking protection against false positives

  • 1. Protection of digital watermarking based on SVD against false positive detection vulnerability UNIVERSITY HADJ LAKHDER -BATNA- SCIENCES FACULTY COMPUTER SCIENCE DEPARTEMENT By: Belferdi Wassila Dr Behloul Ali INTERNATIONAL CONFERENCE ON ADVANCED COMMUNICATION AND INFORMATION SYSTEMS
  • 2. Plan • Introduction: • Singular Value Décomposition: • False Positive Détection Vulnerability: • Proposed Method: • Sharing Secret Principle: • Experiment Results : • Robustness Conditions Of The Proposed Method: • Conclusion and perspectives: 2
  • 3. Introduction • Day by day, the digital watermarking is becoming a promising technique to protect digital data. • It has seen numerous novel article covering new techniques; each one of those techniques have there advantages and inconveniences. • In recent years, the techniques using linear algebra has attracted attention of researchers to using it for watermarking(e.g. SVD). 3
  • 4. • From the viewpoint of linear algebra we can observe that a discrete image is an array of non-negative scalar entries which may be regarded as a matrix. M=USV’=S li UiVi 4 Singular Value Décomposition (1) M U S V’ m n = m r n
  • 5. 5 SVD Based Watermarking Example Watermark Hôst image Uw Sw Vw U S V Insertion of watermark in hôst image Watermarked Image
  • 6. False Positive Détection Vulnerability • Duo to the watermark insertion method, another watermark rather than the original can be reconstructed as the embedded watermark; causing the false positive detection vulnerability . • If an attacker use U* and V* matrices of his own watermark in place of reserved ones he can show his own watermark. 6
  • 7. False Positive Détection Vulnerability (2) 7
  • 8. Proposed Method • To avoid that an attacker reconstruct his watermark using their own matrices U* and V*; the idea is to share a secret key D between matrices Uw, Sw and Vw of watermark. • During the embedding phase, for each matrices Uw, Sw and Vw of watermark a key is inserted, then use the modified S* to reconstruct the watermarked image, those keys are calculated using sharing secret principle. 8
  • 9. Sharing Secret Principle • The goal is to divide a secret key D into pieces. • The coefficients a1... ak-1 are randomly chosen from a uniform distribution over the integers in [0, p-1] • Pick a random k-1 degree polynomial q(x)=a0+alx+ . . . ak-1xk-1 in which a0=D. • The values D1,..., Dn are evaluate: D1= q(1) ,..., Di = q(i) ,..., Dn = q(n). • Given any subset of k of these Di, we can find the coefficients of q(x) by interpolation, and evaluate D=q (0). 9
  • 12. Experiment Results 12 Robustness results of the proposed method against attacks Attacks We W *e |D-Dc| |D-D*c| Cropping 5 188037 Gaussian noise 5 1888037 Rotation 0,2° 46568 234611 Resizing 44095 232139 Contrast adjustment 425 188469
  • 13. 13 Experiment Results(2) the size of image We W*e |D-Dc| |D-D*c| 512×512 5 188037 256×256 36 188007 128×128 913 187131 64×64 2406 185638 Results of the influence of image size on the robustness of the proposed method
  • 14. Robustness Conditions Of The Proposed Method 14 • In the SVD, the biggest part of energy is concentrated in low frequencies of images, the watermarking profit this property to insert watermark in low and middle frequencies according to robustness/invisibility compromiser influence of the inserted secret on the quality of the watermarked image
  • 15. Conclusion • The novelty of our scheme is the use of the sharing secret principle in watermark embedding, that is adaptively chosen according to the local features of the image. • The aim of our solution is to hide the watermark and insure their robustness against the false positive detection vulnerability. • The experimental results obtained give evidence that our scheme is robust against several attacks. 15
  • 16. Perspectives Our perspective turns around: • increasing the robustness of our solution using block based SVD scheme. • Conceive a méthode to chose coefficients a1... ak-1 randomly to obtain better results. 16