基于双树复小波变换的图像盲水印算法.

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 theComplex Wavelet Domain. First, a watermark image as copyright sign is preprocessed with a random loca

2、tion 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 pro

3、cessing 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 ke

4、ys: 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.I. INTRODUCTIO

5、Nultimedia 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 criti

6、cally sampled domain (The Discrete Real Wavelet Transform (DWT, the DiscreteCosine 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 absence

7、 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 wa

8、velet 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. Kingsbury ' s comple-xtrdeueawl avelet transform (DT-CWT is an outstanding examp

9、le 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, Moro

10、cco (e-mail: mabtoul_samirayahoo.fr.E. Ibn-Elhaj is with the National Institute of Telecommunication (INPT, Rabat,Morocco ( phone: (+212 37 77 30 79 ; fax : (+212 37 77 30 44 e-mail: ibnelhajinpt.ac.ma.D. Aboutajdine is with the GSCM, University Mohamed V, Rabat, Morocco (e-mail: aboutajfsr.ac.ma.II

11、. T HE P ROPOSED M ETHODA. Watermark image disorder preprocessingThe first ste p con sists to cha nge the watermark image W , which is a binary image -1, 1, into a p seudo ran dom matrix W d by using the follow ing equati on:K: W ? Wd , Wd (K(i, j = W(i, j; i, j N (1Where K p rese nt the first key i

12、n our watermark p rocedure, which is an exclusive key to recreate the watermark image. Figure 1 visualizes an exa mple of watermark imagedisorder.Original watermark image Disorder watermark imageFig. 1. The orig inal and disorder watermark image.B. Watermark embeddi ngThe origi nal image is tran sfo

13、rmed in the comp lex wavelet domai n by using DT-CWT 5. The watermark image is cha nged into a p seudo ran dom matrix W d , the n its ada ptively sp read sp ectrum W k and add into low p ass subba nd from fin al level. Figure 2 shows a block diagram of the prop osed watermark embedd ing.IDT-OTT»

14、;OricinAL wd4Bntt k irtugeirr-avrdfisdbinwge Dijoi4jerW4i4£RUt1c inu£« w*Watemiiiicsdmidge寿览iA jpectnuu wileiHiatItWiFig. 3. Image detecti on schemeImage detect ion algorithm Fig. 2. Image embeddi ng schemeImage embeddi ng algorithm 1 DT-CWT: p erform a 2-level Dual Tree Com plexWavel

15、et on orig inal image I orig . The DT-CWT coefficie nts are deno ted by. 2Gen erated the sp read sp ectrum watermark Wk : foreach pi xel (i, j of the low p ass image from fin al level in the value is comp ared with those of its eight n eighbors, t deno tes the total nu mber which the value is larger

16、 than its n eighbors, as described by the follow ing formula:览7& P叫Jr r1 ” "亿OrigLtialOngjual iinagp L 呷DT-CWT de51 image 丁一 "IIII'1 , > 4 and Wd d (i, j = 1W k (i, j = -1The sp read sp ectrum watermark W k p rese nt the sec ond key of our image watermark ing scheme.3 Embedded w

17、atermark: the sp read sp ectrum watermark seque nee W k is embedded by the followi ng rule:?I (i，j =I (i，j +a . W k (i , j . I (i , j (3Where: ?I : are the watermarked DTCWT coefficie nts.-I : are the orig inal DT-CWT coefficie nts.W a : is an intensity parameter of image watermark.k : is the sp rea

18、d sp ectrum watermark image seque nee. 4 IDT-CWT: by the inv erseDT-CWT, we obta in the watermarked image.C. Watermark detect ionWatermark detection is accomplished without referring to the original image and the original watermark image. Figure 3 shows a watermark detection scheme.1 The DT-CWT is p

19、erformed on watermarked image. ?Idenote the DT-CWT coefficients. 2 Constructed Watermark image disorderWdenotes the total number?d : for each embed watermark pixel in?I, its value is compared with those of its eight neighbors; twhich the value is larger than its neighbors. Disorder watermark image c

20、an be formed as:1 if (t> 4 and Wk (i, j = 1W ?d (i, j ' < 4 and Wk (-i1, j =3 Reconstructed watermark imageW ?: the reconstructed watermarmk ai geW? is obtained by using the inverse transform of the preprocessing with the first key.III. R ESULTS AND ANALYSIS Our proposed scheme has been te

21、sted 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 tested tree test images (

22、"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 the reconstructed waterm

23、ark 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 watermarked image and the

24、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 watermarking. This sche

25、me applies the DualTree 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 gathered in figure 6. ha

26、ve confirmed that this new scheme has high fidelity and In the first simulation, we tested the scheme robustness it ' s robust against JPEG compression, igceaotmtaectkrs under different PSNR situation. Figure 5.a show a typical (scaling, remove line and rotation with small angle and result. Resu

27、lts show that we can still correctly detect the signal processing (PSNR, median filter introduced inwatermark under these types of PSNR attacks (figure 6.a. The results obtained withDT-CWT transform are better than the results obtained with DWT transform.We tested the robustness against median filte

28、r. 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 theDWT transform, we can ' t dete

29、ctthe 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 dropping, which

30、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 tested the robu

31、stness 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. Without synch

32、ronization 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.StirMark. A CKN

33、OWLEDGMENTThe 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 SecurityTechnology, 1

34、999. 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 original image,"

35、; in International Conference on Image Processing, vol. III, pp.A robust digital image520-523, 1997. 4 D. Kundur and D. Hatzinakos, watermarkingmethod using wavelet-based fusion, ” in Proc. IEEE Int. Conf. Image Processing 1997 (IC IP 97, vol. 1, Santa Barbara, CA, Oct. 1997, pp. 544547.5 N.G. Kings

36、bury,Complex wavelets for shift invariant analysis and filtering ofsignals ”, Applied Computational Harmonic Anal, vol. 10,“Overco mpl eteno. 3, pp. 234253, May 2001. 6 T H Reeves and N G Kin gsbury.image cod ing usingiterative projection-based noise shaping” , ICIP 02, Rochester, NY, Sept 2002.7 Z. Huai-yu, L. Yi ng and C. Wu: A bli nd sp atial-te mporal algorithm based on 3D wavelet for video watermarki ng. ICME 2004: 1727-1730.8 F. A. P. P etitcolas, R. J. An ders on, and M. GKuh n,“ Attacks oncop y