Abstract

We present a new adaptive blind watermarking scheme using a gray-level computer generated hologram in the discrete wavelet transform domain. By utilizing an improved fuzzy clustering technique and human visual system , the watermark can be adaptively embedded according to block classification. To keep imperceptibility and robustness, a novel iterative embedding algorithm is adopted to change the to-be-embedded coefficients. Compared with the standard Fuzzy c-means (FCM) clustering, the suggested improved FCM (IFCM) converges more quickly and can avoid local optimum effectively. The experimental results demonstrate that the proposed scheme provides good robustness to withstand different kinds of common attack. Compared with other methods, the proposed method has the distinct advantage of better robustness to a JPEG compression attack.

© 2009 Optical Society of America

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References

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  1. I. J. Cox and M. L. Miller, “The first 50 years of electronic watermarking,” J. Appl. Signal Process. 1, 126-132(2002).
    [CrossRef]
  2. A. Reddy and B. Chatterji, “A new wavelet based logo-watermarking scheme,” Pattern Recogn. Lett. 26, 1019-1027(2005).
    [CrossRef]
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    [CrossRef]
  4. S. Kishk and B. Javidi, “3D object watermarking by a 3D hidden object,” Opt. Express 11, 874-888 (2003).
    [CrossRef] [PubMed]
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    [CrossRef] [PubMed]
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    [CrossRef] [PubMed]
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    [CrossRef]
  9. T. Kreis, Holographic Interferometry: Principles and Methods, 1st ed. (Akademie-Verlag, 1996).
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    [CrossRef] [PubMed]
  11. W. C. Chen and M. S. Wang, “A fuzzy c-means clustering-based fragile watermarking scheme for image authentication,” Expert Syst. Appl. 36, 1300-1307 (2009).
    [CrossRef]
  12. T. Kim and J. C. Bezbek, “Optimal tests for the fixed points of the fuzzy c-means algorithms,” Pattern Recogn. 21, 651-663(1988).
    [CrossRef]
  13. J. Z. Wu and J. Y. Xie, “Blind wavelet-based watermarking scheme using fuzzy clustering theory,” in the 2003 International Conference on Neural Networks and Signal Processing (IEEE, 2003), pp. 1521-1524.
  14. Y. Z. Shen, M. J. Zhang, and F. Liu, “A new algorithm of gray watermark embedding,” in Advances in Artificial Reality and Tele-Existence: 16th International Conference on Artificial Reality and Telexistence (Springer, 2006), pp. 796-801.
  15. C. Iemmi, S. Ledesma, J. Campos, and M. Villarreal, “Gray-level computer-generated hologram filters for multiple-object correlation,” Appl. Opt. 39, 1233-1240 (2000).
    [CrossRef]
  16. R. A. Fisher, “Iris data set,” http://archive.ics.uci.edu/ml/datasets/Iris.
  17. I. C. Yeh, “Blood Transfusion Service Center data set,” http://archive.ics.uci.edu/ml/datasets/Blood+Transfusion+Service+Center.
  18. P. Benedict, “DGP2--the Second Data Generation Program data set,” http://archive.ics.uci.edu/ml/datasets/DGP2+-+The+Second+Data+Generation+Program.

2009 (1)

W. C. Chen and M. S. Wang, “A fuzzy c-means clustering-based fragile watermarking scheme for image authentication,” Expert Syst. Appl. 36, 1300-1307 (2009).
[CrossRef]

2007 (1)

2005 (3)

2003 (1)

2002 (2)

I. J. Cox and M. L. Miller, “The first 50 years of electronic watermarking,” J. Appl. Signal Process. 1, 126-132(2002).
[CrossRef]

N. Takai and Y. Mifune, “Digital watermarking by a holographic technique,” Appl. Opt. 41, 865-873 (2002).
[CrossRef] [PubMed]

2000 (2)

1988 (1)

T. Kim and J. C. Bezbek, “Optimal tests for the fixed points of the fuzzy c-means algorithms,” Pattern Recogn. 21, 651-663(1988).
[CrossRef]

1987 (1)

Akar, G. B.

Benedict, P.

P. Benedict, “DGP2--the Second Data Generation Program data set,” http://archive.ics.uci.edu/ml/datasets/DGP2+-+The+Second+Data+Generation+Program.

Bezbek, J. C.

T. Kim and J. C. Bezbek, “Optimal tests for the fixed points of the fuzzy c-means algorithms,” Pattern Recogn. 21, 651-663(1988).
[CrossRef]

Campos, J.

Chang, H. T.

Chatterji, B.

A. Reddy and B. Chatterji, “A new wavelet based logo-watermarking scheme,” Pattern Recogn. Lett. 26, 1019-1027(2005).
[CrossRef]

Chen, W. C.

W. C. Chen and M. S. Wang, “A fuzzy c-means clustering-based fragile watermarking scheme for image authentication,” Expert Syst. Appl. 36, 1300-1307 (2009).
[CrossRef]

Cox, I. J.

I. J. Cox and M. L. Miller, “The first 50 years of electronic watermarking,” J. Appl. Signal Process. 1, 126-132(2002).
[CrossRef]

Fisher, R. A.

R. A. Fisher, “Iris data set,” http://archive.ics.uci.edu/ml/datasets/Iris.

Iemmi, C.

Javidi, B.

Kim, H.

Kim, T.

T. Kim and J. C. Bezbek, “Optimal tests for the fixed points of the fuzzy c-means algorithms,” Pattern Recogn. 21, 651-663(1988).
[CrossRef]

Kishk, S.

Kreis, T.

T. Kreis, Holographic Interferometry: Principles and Methods, 1st ed. (Akademie-Verlag, 1996).

Ledesma, S.

Lee, Y. H.

Liu, F.

Y. Z. Shen, M. J. Zhang, and F. Liu, “A new algorithm of gray watermark embedding,” in Advances in Artificial Reality and Tele-Existence: 16th International Conference on Artificial Reality and Telexistence (Springer, 2006), pp. 796-801.

Mifune, Y.

Miller, M. L.

I. J. Cox and M. L. Miller, “The first 50 years of electronic watermarking,” J. Appl. Signal Process. 1, 126-132(2002).
[CrossRef]

Nomura, T.

Okman, O. E.

Reddy, A.

A. Reddy and B. Chatterji, “A new wavelet based logo-watermarking scheme,” Pattern Recogn. Lett. 26, 1019-1027(2005).
[CrossRef]

Shen, Y. Z.

Y. Z. Shen, M. J. Zhang, and F. Liu, “A new algorithm of gray watermark embedding,” in Advances in Artificial Reality and Tele-Existence: 16th International Conference on Artificial Reality and Telexistence (Springer, 2006), pp. 796-801.

Takai, N.

Tricoles, G.

Tsan, C. L.

Villarreal, M.

Wang, M. S.

W. C. Chen and M. S. Wang, “A fuzzy c-means clustering-based fragile watermarking scheme for image authentication,” Expert Syst. Appl. 36, 1300-1307 (2009).
[CrossRef]

Wu, J. Z.

J. Z. Wu and J. Y. Xie, “Blind wavelet-based watermarking scheme using fuzzy clustering theory,” in the 2003 International Conference on Neural Networks and Signal Processing (IEEE, 2003), pp. 1521-1524.

Xie, J. Y.

J. Z. Wu and J. Y. Xie, “Blind wavelet-based watermarking scheme using fuzzy clustering theory,” in the 2003 International Conference on Neural Networks and Signal Processing (IEEE, 2003), pp. 1521-1524.

Yeh, I. C.

I. C. Yeh, “Blood Transfusion Service Center data set,” http://archive.ics.uci.edu/ml/datasets/Blood+Transfusion+Service+Center.

Zhang, M. J.

Y. Z. Shen, M. J. Zhang, and F. Liu, “A new algorithm of gray watermark embedding,” in Advances in Artificial Reality and Tele-Existence: 16th International Conference on Artificial Reality and Telexistence (Springer, 2006), pp. 796-801.

Appl. Opt. (4)

Expert Syst. Appl. (1)

W. C. Chen and M. S. Wang, “A fuzzy c-means clustering-based fragile watermarking scheme for image authentication,” Expert Syst. Appl. 36, 1300-1307 (2009).
[CrossRef]

J. Appl. Signal Process. (1)

I. J. Cox and M. L. Miller, “The first 50 years of electronic watermarking,” J. Appl. Signal Process. 1, 126-132(2002).
[CrossRef]

J. Opt. Soc. Am. A (1)

Opt. Express (2)

Opt. Lett. (1)

Pattern Recogn. (1)

T. Kim and J. C. Bezbek, “Optimal tests for the fixed points of the fuzzy c-means algorithms,” Pattern Recogn. 21, 651-663(1988).
[CrossRef]

Pattern Recogn. Lett. (1)

A. Reddy and B. Chatterji, “A new wavelet based logo-watermarking scheme,” Pattern Recogn. Lett. 26, 1019-1027(2005).
[CrossRef]

Other (6)

T. Kreis, Holographic Interferometry: Principles and Methods, 1st ed. (Akademie-Verlag, 1996).

J. Z. Wu and J. Y. Xie, “Blind wavelet-based watermarking scheme using fuzzy clustering theory,” in the 2003 International Conference on Neural Networks and Signal Processing (IEEE, 2003), pp. 1521-1524.

Y. Z. Shen, M. J. Zhang, and F. Liu, “A new algorithm of gray watermark embedding,” in Advances in Artificial Reality and Tele-Existence: 16th International Conference on Artificial Reality and Telexistence (Springer, 2006), pp. 796-801.

R. A. Fisher, “Iris data set,” http://archive.ics.uci.edu/ml/datasets/Iris.

I. C. Yeh, “Blood Transfusion Service Center data set,” http://archive.ics.uci.edu/ml/datasets/Blood+Transfusion+Service+Center.

P. Benedict, “DGP2--the Second Data Generation Program data set,” http://archive.ics.uci.edu/ml/datasets/DGP2+-+The+Second+Data+Generation+Program.

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Figures (7)

Fig. 1
Fig. 1

Block diagram of the choosing procedure for the initial cluster centers of the IFCM.

Fig. 2
Fig. 2

Embedded position and its eight neighbor pixels in a to-be-embedded block.

Fig. 3
Fig. 3

Block diagram of the embedding procedure for an element w of the 1D array w m being embedded into one selected block.

Fig. 4
Fig. 4

Sample watermark generated by computer: (a) original 2D image “snow” of size 64 × 64 ; (b) Fourier transform CGH of the “snow” image; (c) reconstruction of the hologram.

Fig. 5
Fig. 5

Sample of watermarked image using the proposed method: (a) original image; (b) watermarked image; (c) mark hologram extracted from (b); (d) reconstruction of (c).

Fig. 6
Fig. 6

Reconstructions of extracted holograms under attacks of noise addition, filtering and cropping, and rotation: (a) Gaussian noise; (b) salt and pepper noise; (c) Gaussian low-pass filtering; (d) average filtering; (e) median filtering; (f) circular average; (g) horizontal motion; (h) sharpness; (i) contrast enhancement; (j) cropping of size 50%; (k) rotation 1 ° .

Fig. 7
Fig. 7

Comparison of the NC s of the extracted watermarks from the proposed scheme and the Okman scheme using a 48 bin quantizer after JPEG attack.

Tables (2)

Tables Icon

Table 1 Comparison of the Standard FCM and the Improved FCM

Tables Icon

Table 2 Attacking Test of the Proposed Scheme

Equations (19)

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F x y = Re x y + j Im x y ,
I x y = ( Re x y 2 + Im x y 2 ) 1 / 2 , φ x y = arctan [ Im x y / Re x y ] .
T x y = 0.5 [ 1 + I x y cos ( 2 π α x φ x y ) ] ,
J ( U , V ) = i = 1 m j = 1 n u i j γ x j v i 2 ,
i = 1 m u i j = 1 and u i j [ 0 , 1 ] ,
v i ( z ) = ( j = 1 n ( u i j ( z 1 ) ) γ x j ) / j = 1 n ( u i j ( z 1 ) ) γ , 1 i m .
u i j ( z ) = 1 / k = 1 m ( x j v i ( z ) / x j v k ( z ) ) 2 / ( γ 1 ) , 1 i m , 1 j n .
d i j 2 = ( x i x j ) T ( x i x j ) = x i x j 2 = k = 1 s ( x i k x j k ) 2 ,
y c j = 1 p j x i c j x i , j = 1 , 2 , , m ,
B ( i , j ) = 1 9 u , v = 1 g ( i + u , j + v ) ,
T ( i , j ) = 1 9 u , v = 1 1 | g ( i + u , j + v ) B ( i , j ) | ,
C ( i , j ) = max u , v = 1 , 0 , 1 g ( i + u , j + v ) min u , v = 1 , 0 , 1 g ( i + u , j + v ) ,
f ( i , j ) = ( B ( i , j ) , T ( i , j ) , C ( i , j ) ,
U = Y + k Δ .
U = | A | + w ,
w = Y + k Δ | A | .
PSNR = 10 log 10 D 2 M N m = 1 M n = 1 N ( I ( m , n ) I w ( m , n ) ) 2 dB ,
N C = W emb T W ext W emb T W emb .
σ I = ( I ext I emb ) 2 1 / 2 I emb ,

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