基于雙樹復(fù)小波變換的圖像盲水印算法

1、A Blind Image Watermarking Algorithm Based on Dual TreeComplex Wavelet TransformS. Mabtoul , E. Ibn-Elhaj, D. AboutajdineAbstract This paper presents a watermarking procedure for digital image in the Complex Wavelet Domain. First, a watermark image as copyright sign is preprocessed with a random loc

2、ation matrix. The original image is transformed in the complex wavelet domain by using DT-CWT, then, according to the characteristics of the image data, the preprocessed watermark image is adaptively spread spectrum and added into the host image DT-CWT coefficients. The superior results for image pr

3、ocessing applications compared to the DWT 5, 6.In the proposed scheme, we applied the Dual Tree Complex Wavelet Transform; the watermark image is preprocessed with a random matrix, adaptively spread spectrum 7 and added into the host image DT-CWT posed watermark algorithm needs two k

4、eys: a random location matrix ensures the security of watermarking procedure and spread spectrum watermark sequence guarantees its robustness. Simulation results demonstrate the robustness of our image watermarking procedure, especially under the typical attacks of geometric operations.MI. INTRODUCT

5、IONultimedia watermarking technology has evolved very quickly during the last few years. A digital watermark is information that is imperceptibly and robustly embedded in the host data such that it cannot be removed 1, 2.There are several watermarking algorithms transform the original image into cri

6、tically sampled domain (The Discrete Real Wavelet Transform (DWT, the Discrete Cosine Transform (DCT or the Discrete Fourier Transform (DFT, and add a random sequence to the transformed image coefficients 3, 4.In general, the DWT produces watermark images with the best visual quality due to the abse

7、nce of blocking artifacts. However, it has two drawbacks:-Lack of shift invariance, which means that small shifts in the input signal can cause major variations in the distribution of energy between DWT coefficients at different scales.-Poor directional selectivity for diagonal features, because the

8、 wavelet filters are separable and real.An important recent development in wavelet-related research is the design and implementation of 2-D multiscale transforms that represent edges more efficiently than does the DWT. Kingsburys complex dual-tree wavelet transform (DT-CWT is an outstanding example

9、5. The DT-CWT is an overcomplete transform with limited redundancy (2m: 1 for m-dimensional signals. This transform has good directional selectivity and its subband responses are approximately shift-invariant. The 2-D DT-CWT has given S. Mabtoul is with the GSCM, University Mohamed V, Rabat, Morocco

10、 (e-mail: mabtoul_samirayahoo.fr.II. T HE P ROPOSED M ETHODA. Watermark image disorder preprocessingThe first step consists to change the watermark image W , which is a binary image -1, 1, into a pseudo random matrix W d by using the following equation:K: W Î Wd , Wd (K(i, j = W(i, j; i, jN (1W

11、here K present the first key in our watermark procedure, which is an exclusive key to recreate the watermark image. Figure 1 visualizes an example of watermark image disorder. Original watermark image Disorder watermark image Fig. 1. The original and disorder watermark image.B. Watermark embeddingTh

12、e original image is transformed in the complex wavelet domain by using DT-CWT 5. The watermark image is changed into a pseudo random matrix W d , then its adaptively spread spectrum W k and add into low pass subband from final level. Figure 2 shows a block diagram of the proposed watermark embedding

13、. Fig. 3. Image detection schemeImage detection algorithm Fig. 2. Image embedding schemeImage embedding algorithm 1 DT-CWT: perform a 2-level Dual Tree Complex Wavelet on original image I orig . The DT-CWT coefficients are denoted by. 2 Generated the spread spectrum watermark Wk : foreach pixel (i,

14、j of the low pass image from final level in , the value is compared with those of its eight neighbors, t denotes the total number which the value is larger than its neighbors, as described by the following formula: 4 and Wd d (i, j = 1W k (i, j = -1The spread spectrum watermark W k present the secon

15、d key of our image watermarking scheme.3 Embedded watermark: the spread spectrum watermark sequence W k is embedded by the following rule:I (i , j =I (i , j +. W k (i , j . I (i , j (3Where: I : are the watermarked DT-CWT coefficients. I : are the original DT-CWT coefficients.W : is an intensity par

16、ameter of image watermark.k : is the spread spectrum watermark image sequence. 4 IDT-CWT: by the inverse DT-CWT, we obtain the watermarked image.C. Watermark detectionWatermark detection is accomplished without referring to the original image and the original watermark image. Figure 3 shows a waterm

17、ark detection scheme.1 The DT-CWT is performed on watermarked image. Idenote the DT-CWT coefficients. 2 Constructed Watermark image disorderWd : for each embed watermark pixel inI, its value is compared with those of its eight neighbors; t denotes the total number which the value is larger than its

18、neighbors. Disorder watermark image can be formed as:1 if (t 4 and Wk (i, j = 1W d (i, j < 4 and Wk (i, j = -13 Reconstructed watermark imageW : the reconstructed watermark imageW is obtained by using the inverse transform of the preprocessing with the first key. III. R ESULTS AND ANALYSIS Our pr

19、oposed scheme has been tested under variousattacks. We chose to test this scheme under PSNR, median filter, JPEG compression, remove lines and scaling attacks introduced by Stirmark 8 and also rotation attack. We have performed the algorithm under Matlab 6.5 environment. In the experiments, we have

20、tested tree test images ("Lena", "Barbara" and "Cameraman", and there have the similarresults. Here, we use "Lena" as an example and the watermark is a binary image with the size of 128x128 pixels.Figure 4 presents the original image, the watermarked image and

21、 the reconstructed watermark image, in which the watermark intensity factor equal 0.004. We see that the watermarked image is not distinguishable from the originalimage. Original image Watermarked image Reconstructed watermark(256x256 pixels (256x256 pixels image (128x128pixelsFig. 4. Original and w

22、atermarked image and the reconstructedwatermark image.The robustness of watermarking is measured by thesimilarity of the detected watermark W and the originalwatermark W , which is defined as:IV. C ONCLUSIONIn this paper, we have proposed a novel scheme of image, W =(i,j.W(i,j Sim ( W (W (W(i,j (5 w

23、atermarking. This scheme applies the Dual Tree Complexi j i j Wavelet Transform; the watermark image is preprocessedwith a random matrix, adaptively spread spectrum and addedWe tested this watermark approach with DWT transform; into the DT-CWT coefficients. The experimental results the results are g

24、athered in figure 6. have confirmed that this new scheme has high fidelity and In the first simulation, we tested the schemes robustness its robust against JPEG compression, geometric attacks under different PSNR situation. Figure 5.a show a typical (scaling, remove line and rotation with small angl

25、e and result. Results show that we can still correctly detect the signal processing (PSNR, median filter introduced in 2watermark under these types of PSNR attacks (figure 6.a. The results obtained with DT-CWT transform are better than the results obtained with DWT transform.We tested the robustness

26、 against median filter. Figure 5.bhas shown a typical result. The similarities of original watermark and reconstructed watermark are shown in figure 6.b. We noticed that we can still correctly detect the watermark with the algorithm used the DT-CWT transform. With the algorithm used the DWT transfor

27、m, we cant detectthe watermark if the filter factor is bigger than 7.We tested this scheme when the image undergone a scaling (see figure 5.c. The results are shown in figure 6.c. from the results obtained we notices that we can detect thewatermark image if we used the DT-CWT or the DWT. The lines d

28、ropping, which are some lines are removedfrom the watermarked image. We tested this scheme against this type of attack (see figure 5.d. The experiment result is plotted in figure 6.d. The results show that we canreconstruct the watermark image correctly if we used the DT-CWT or the DWT.We have also

29、tested the robustness against JPEG compression (see example in figure 5.e. The corresponding results are presented in figure 6.e. this scheme is robustnessagainst this type of attack.We evaluated the robustness of this scheme against rotation attacks. Image rotation makes the coordinate axes changed

30、. Without synchronization of orthogonal axes, we cannot reconstruct the image mark correctly Figure 5.f illustrates the effect of this transformation. The results are shown in figure 5.f. according to the results we notices that we can reconstruct correctly the watermark image if we used the DT-CWT.

31、StirMark. A CKNOWLEDGMENTThe authors would like to thank Dr. Nick Kingsbury forallowing use to use his DT-CWT algorithm, and for hisvaluable discussions. R EFERENCES 1 F. P. Gonzalez & Juan R. Hernandez, " A tutorial on digitalwatermarking", In IEEE Annual Carnahan Conference on Securi

32、tyTechnology, 1999. 2 Ingemar J. Cox, Matt L. Miller, " The first 50 years of electronic watermarking", Journal of Applied Signal Processing, 2, 126-132,2002.3 A. Piva, M. Barni, F. Bartolini, and V. Cappellini, "DCT-basedwatermark recovering without restoring to the uncorrupted origi

33、nal image," in Inter-national Conference on Image Processing, vol. III, pp.520-523, 1997. 4 D. Kundur and D. Hatzinakos, “A robust digital image watermarkingmethod using wavelet-based fusion,” in Proc. IEEE Int. Conf. Image Processing 1997 (ICIP 97, vol. 1, Santa Barbara, CA, Oct. 1997, pp. 544

34、547.5 N.G. Kingsbury, “Complex wavelets for shift invariant analysis and filtering of signals”, Applied Computational Harmonic Anal, vol. 10,no. 3, pp. 234-253, May 2001. 6 T H Reeves and N G Kingsbury, “Overcomplete image coding usingiterative projection-based noise shaping”, ICIP 02, Rochester, NY, Sept 2002.7 Z. Huai-yu, L. Ying and C. Wu: A blind spatial-temporal algorithm based on 3D wavelet for video watermarking. ICME 2004: 1727-1730.8 F. A. P. Petitcolas, R. J. Anderson, and M. G. Kuhn, “Attacks oncopyright marking systems,” in Lectur