CEW: A Non-Blind Adaptive Image Watermarking ... - Semantic Scholar

CEW: A Non-Blind Adaptive Image Watermarking Approach Based on Entropy in Contourlet Domain Shiva Zaboli and Mohammad Shahram Moin Multimedia Research Group, IT Faculty Iran Telecommunications Research Center Tehran, Iran Email: [email protected], [email protected] Abstract— This paper presents a new approach for non-blind watermarking of still gray level images in contourlet domain. It uses the Human Visual System (HVS) characteristic, and an innovative entropy based approach to create an efficient watermarking scheme. It decomposes original image in contourlet domain in four hierarchical levels and watermarks it with a logo image, which is scrambled thru a well-known PN-sequence. A novel entropy-based method is developed for selection of contourlet coefficients that provides an adaptive way for determining the number of watermarked coefficients and watermarking factor at each level of contourlet decomposition. This approach shows resistance against some attacks presented in the watermarking literature.

I.

INTRODUCTION

Currently, almost all multimedia production and distributions are digital. The advantages of digital media for creation, processing and distribution of productions are well known: easy modification and possibility of software processing rather than the more expensive hardware alternative (if real-time processing is not a requirement). One of its most important features is the possibility of unlimited copying of digital data without any loss of quality, which is not desirable for media producers and content providers. In fact, it is perceived as a major threat, because it may cause them considerable financial loss. Digital watermarks have been proposed as a way to tackle this problem. This digital signature could discourage copyright violation, and may help determining the authenticity and ownership of an image. Ideal characteristics of a digital watermark have been stated in [1]. These characteristics include: • Perceptual invisibility. • Statistical invisibility. • Fairly simple extraction. • Accurate detection. • Robustness to filtering, additive noise, compression, and other image manipulations. • Ability to determine the true owner of the image. Early work on digital watermarking for still images focused on information hiding in the spatial domain. Recent efforts are mostly based on frequency-domain techniques. Many watermarking methods have been proposed in the literature.

1-4244-0755-9/07/$20.00 '2007 IEEE

Cox et al. [2] noted that in order for a watermark to be robust to attacks, it must be placed in perceptually significant areas of the image. The watermark was based on 1000 random samples of a N(0,1) distribution. These samples were added to the 1000 largest Discrete Cosine Transform (DCT) coefficients of the original image, and the inverse DCT was taken to retrieve the watermarked image. Xia, Boncelet, and Arce [3] proposed a watermarking scheme based on the Discrete Wavelet Transform (DWT). The watermark, modeled as Gaussian noise, was added to the middle and high frequency bands of the image. The decoding process involved in taking the DWT of a potentially marked image. Bartolini et al. [4] first generated a watermarked image from DCT coefficient, then spatial masking was performed on the new image to hide the watermark. Kundur and Hatzinakos [5] embedded the watermark in the wavelet domain. The strength of the watermark was determined by the contrast sensitivity of the original image. Both techniques showed resistance to common signal processing operations. Delaigle et al. [6] proposed a unique watermarking scheme based on the Human Visual System. Binary msequences were generated and then modulated on a random carrier. This image served as the watermark, and then it was masked based upon the contrast between the original signal and the modulated image. Their technique was robust to additive noise, JPEG coding, and rescanning. Safabakhsh et al. [7] used the Human Visual System (HVS) characteristic, and an innovative entropy based approach to create an efficient watermarking scheme. It decomposes original image in DWT domain in to three hierarchical levels and watermarks it with a logo image, which is scrambled thru a well-known Polynomial-sequence. An entropy-based method is developed for selection of DWT coefficients that provides an adaptive way for determining the number of watermarked coefficients and watermarking factor at each level of DWT decomposition. This approach shows good resistance against almost all known attacks in the watermarking literature. Bas, Chassery, and Davoine [8] introduced a watermarking system using fractal codes. The watermark was added to a collage map was composed of 8x8 blocks of an original image and its DCT, to produce a marked image. Results showed that a fractal coding in the DCT domain performed better than in the spatial domain. The DCT-based watermarking technique was robust to JPEG compression, while spatial fractal coding produced block artifacts after compression. Although different transforms (e.g., DFT, DCT,

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and DWT) have been used in digital watermarking schemes reported in the literature, there is no common framework for multiresolution digital watermarking of both images and video. The existing transform domain techniques locate regions of high frequency or middle frequency to embed information imperceptibly. As already mentioned, the transforms usually used for digital watermarking are DCT, Discrete Fourier Transform (DFT) and DWT. Amongst the proposed algorithms so far wavelet domain algorithms perform better than DCT based algorithms. It has been proved that wavelets are good at representing discontinuities in one dimension or point singularities. But, since in higher dimensions, there are more types of singularities which wavelets fail to represent, we need to go for transforms like curvelets and contourlets for better performance. Curvelet transform was defined in the continuum space R2 and its discretization is a challenge when critical sampling is desired [9]. Contourlet transform was proposed as an improvement on curvelet transform using a double filter bank structure [10]. Contourlet transform provides a flexible multiresolution representation for signals. One of the unique properties of contourlet transform is that we can specify the number of directional decompositions required at every level of multiresolution pyramid. In [10] the first algorithm proposes decomposition scheme where the number of directional bands doubles at every scale of multiresolution pyramid. Watermark embedding with a different decomposition which obeys the curve scaling relation of curvelets is presented in the second algorithm. It was observed that contourlet based methods performed much better than wavelet and DCT based methods in images like maps [10]. In this paper a new approach for non-blind watermarking of still gray level images in contourlet domain is prersented. The method uses the Human Visual System (HVS) characteristic, and an innovative entropy based approach to create an efficient watermarking scheme. It decomposes original image in contourlet domain in four hierarchical levels and watermarks it with a logo image, which is scrambled thru a well-known PNsequence. A novel entropy-based method is developed for selection of contourlet coefficients that provides an adaptive way for determining the number of watermarked coefficients and watermarking factor at each level of contourlet decomposition. The paper covers a brief introduction to contourlet transform in Section II. Section III describes the proposed algorithms. Performance Evaluation and conclusion are included in Section IV and Section V respectively. II.

containing contours and textures. It is constructed by combining two distinct and successive decomposition stages: a multiscale decomposition followed by a directional decomposition. The first stage uses a Laplacian pyramid scheme to transform the image into one coarse version plus a set of LP bandpass images. The second stage applies appropriately 2-D quincunx filtering and critical subsampling to decompose each LP bandpass image into a number of wedge shaped subbands, and thus capturing directional information. Finally, the image is represented as a set of directional subbands at multiple scales. The contourlet transform is perfect reconstruction and almost critically sampled with a small redundancy factor of up 4/3 due to the Laplacian pyramid. Fig. 1, shows a flow graph of the Contourlet transform. When compared to the discrete wavelet transform, the contourlet transform, with its extra feature of directionality, yields some improvements and new potentials in image processing applications. Indeed, various experiments clearly show that smooth edges are efficiently represented by few local coefficients in the right directional subbands, leading to better representation of fine contours. The above-mentioned statements raise an open question regarding the use of contourlets for image watermarking and to determine how effective it is [12]. This is the primary motivation for this work. As a result of a separable extension from 1-D bases, wavelet in 2-D are good at isolating the discontinuities at edge points, and separable wavelets can capture only limited directional information, which is an important and unique feature of multidimensional signals. III.

THE PROPOSED WATERMARKING SCHEME

The proposed Contourlet Entropy-based Watermark (CEW) embedding and extraction scheme is shown in Fig. 2. In this method the directional decomposition is performed according to curve scaling relation. Here the number of directions in the band pass image is doubled at each scale of decomposition. The decomposition of image into low frequency band L and the directional bands A, B, C and D are shown in Fig. 3.

THE CONTOURLET TRANSFORM

The contourlet transform, as introduced by Minh Do and Martin Vetterli [11] is a new image decomposition scheme, which provides sparse representation at both spatial and directional resolutions. It can efficiently represent images

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Fig. 1. Pyramidal Directional Filter Banks [10]

Embedding

Extraction

Original Image

Watermarked Image

Contourlet Transform

Watermark

XOR

num of selected coeffs

Selected Coeffs

Entropy Calculation

Attack

W ′( x, y ) = W ( x, y ) + αmk

PN Sequence

Inverse Contourlet Transform

Contourlet Transform

HVS Calculation

(W ′ − W )

α

XOR

Initial Seed Similarity Extracted Watermark

Fig. 2. The CEW watermarking scheme

For embedding the original watermark, we select a set of high absolute value coefficients in each level sub-band (A1, A2, …, D3, D4) by adjusting the initialized “chipRate” variable. The value of this variable is defined in an adaptive way by multiplication of entropy of the corresponding detail with an initial constant value that is set by the owner. Additive watermark embedding of the selected pixels are performed according to (1): (1) W ′( x, y ) = W ( x, y ) + αmk

use in the extraction phase. Scrambling the logo image enhances the system security and provides a random distribution of original data.

Where W(x,y) is highest absolute values, selected from bands A, B, C and D. α is the multiplication parameter used for embedding. w′( x, y ) is the corresponding watermarked pixel and m k is the watermark bit obtained after randomizing the selected binary watermark. For embedding the original watermark, we select a set of high value coefficients in each level sub-band by adjusting the initialized “chipRate” variable. The value of this variable is defined in an adaptive way by multiplication of entropy of the corresponding detail with an initial constant value that is set by the owner. The original watermark is extracted from a binary logo image, which is scrambled by a well-known PN-sequence. Initial random seed of this PN-Sequencer should be saved for

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Fig 3. Contourlet decomposition

The highest absolute coefficients in any band would represent a strong edge or a high frequency component in that respective direction. Hence it would be advantageous, as far as transparency is concerned, if we select those pixels. In our simulations, we have selected the highest coefficients which are equal to the size of the watermark. In the watermark extraction, we need a copy of the original image, thus, the CEW algorithm is non-blind. Subtracting the original image pixels from the watermarked pixel in the transform domain gives the scaled watermark. After extracting the watermark, we calculate the normalized correlation coefficient between the extracted watermark ( m′ ) and the original watermark ( m ) to prove authenticity. Suppose there are K pixels in the watermark. Then the normalized correlation coefficient ( γ ) between m and m ′ is defined as follows: K

γ =

∑ m m′ i

i =1

K

i

(2)

K

∑ m ∑ m′ i =1

2 i

i =1

2

i

For watermark detection, we calculate the similarity between the extracted de-scrambled logo, and the original logo. A visible extracted logo -even in one of the sub-bands, itself can be used to prove of watermark detection. Fig. 4. Part of test image

IV.

PERFORMANCE EVALUATION

We used a variety of still 512x512 well-known test images in our experiments, including images of faces, outdoor scenes, and textured, medical, and synthetic ones with variations in illumination, resolution and size. Figure 4, shows some of test images, which used in performance of the proposed approach, are evaluated under several attacks, including cropping, rotation and Gaussian noise. Fig. 5 shows the output of different stages of CEW algorithm under Gaussian noise attack, for baboon image. Difference image determines the location of selected coefficients in contourlet domain. As it can be seen in this image, the watermark is spread in whole image area. Fig. 6, shows the extracted watermark under Gaussian noise attack is very similar to the original logo image, particularly for highest levels, B, C, and D. By increasing the levels of decompositions, the watermarking capacity is also increased and the quality of extracted watermark is improved. Although the image quality is reduced by increasing the watermarking capacity, we compensate it by using the HVS and entropy schemes in insertion phase. Fig. 7, 8, 9 and 10 shows the watermark detection responses in different sub-bands of level D for pepper image among 1000 randomly generated watermarks under Gaussian attack with mean zero and variance 0.001. The original watermark is produced by scrambling the DEPT logo image with the initial seed ‘0000001’. The single large peaks in the responses at different levels are reproduced correctly.

Original Image

Watermarked Image

Difference Image

Attacked Image

Fig. 5. Output images in different stages of the CEW algorithm

Table I shows the performance of our method, presented by corresponding detector response (Max Similarity) of watermarked image, compared with those of two related works, which also used contourlet decomposition. These methods (Method I and Method II) are reported in [10]. As one can see in Table 1, our watermark approach is robust enough against all well known attacks including Gaussian noise, cropping and rotation, and its performance is better than other related methods. Figure 11, 12 and 13 show results of Method I, Method II and CEW technique on well-known image under Gaussian, Cropping and rotation attack respectively.

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Sub-Band 8 @ Level D [detectected] A1

A2

A3

A4

B1

B2

B3

B4

C1

C2

C3

C4

C5

C6

C7

C8

D1

D2

D3

D4

D5

D6

D7

D8

0.8 0.7 0.6

similarity

0.5 0.4 0.3 0.2 0.1 0 -0.1

0

100

200

300

400 500 600 random watermarks

700

800

900

1000

Fig. 10. Similarity values at sub-band 8 in level D for pepper image Fig. 6. Extracted logos for Gaussian noise in different levels of Contourlet decomposition

TABLE I COMPARISON TABLE

Sub-Band 2 @ Level D [detectected] 0.9

Max-Similarity under attacks

0.8

Image Name

0.7 0.6

Watermarking technique

similarity

0.5 0.4

Baboon

0.3 0.2 0.1

Gray

0 -0.1

0

100

200

300

400 500 600 random watermarks

700

800

900

1000

Sat

Fig. 7. Similarity values at sub-band 2 in level D for pepper image City Sub-Band 4 @ Level D [detectected] 0.9

Lena

0.8 0.7 0.6

Pepper

similarity

0.5 0.4 0.3

Barbara

0.2

Method I Method II CEW Method I Method II CEW Method I Method II CEW Method I Method II CEW Method I Method II CEW Method I Method II CEW Method I Method II CEW

Gaussian Cropping noise Rotation 6° (mean,var) = 400x450 (0,0.0001) 0.7550 0.4150 0.4150 0.7925 0.4075 0.4800 0.8711 0.6610 0.7098 0.7225 0.3350 0.5775 0.7325 0.4825 0.6875 0.8652 0.6674 0.8912 0.8025 0.3950 0.5675 0.8150 0.4800 0.5900 0.8924 0.6916 0.8654 0.7725 0.4025 0.4350 0.8375 0.4350 0.4725 0.8753 0.6636 0.7788 0.7575 0.4200 0.5075 0.7850 0.4425 0.5975 0.8701 0.6527 0.8366 0.8000 0.4650 0.5675 0.8275 0.4375 0.6125 0.8758 0.6658 0.8347 0.7850 0.4175 0.5250 0.8250 0.4575 0.5650 0.8677 0.6820 0.7773

0.1 0 -0.1

Max Similarity under Gaussian noise 1 0

100

200

300

400 500 600 random watermarks

700

800

900

1000

0.9

Fig. 8. Similarity values at sub-band 4 in level D for pepper image

0.8 0.7

Sub-Band 6 @ Level D [detectected]

Maximum Similarity

0.8 0.7 0.6

similarity

0.5 0.4

0.6 0.5 0.4 0.3

0.3

0.2

0.2 0.1 0 -0.1

0

100

200

300

400 500 600 random watermarks

700

800

900

1000

0.1

CEW Method I Method II

0 Baboon

Gray

Sat

City Image Name

Lena

Pepper

Barbara

Fig 11. Maximum Similarity of some image under Gaussian Attack

Fig. 9. Similarity values at sub-band 6 in level D for pepper image

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Although the image quality is reduced by increasing the watermarking capacity, we compensate it by using the HVS and entropy schemes in insertion phase. This scheme can be easily extended to color images and with some small modifications in time-consuming processes, it can be used for video image watermarking.

Max Similarity under Croppung attack 1 0.9 0.8

Maximum Similarity

0.7 0.6 0.5

REFERENCES

0.4

[1]

Er-Hsien Fu, “Literature Survey on Digital Image Watermarking”. EE381K Multimdimensional Signal Processing, 19 Aug. 1998.

[2]

Ingemar J. Cox, Joe Kilian, Tom Leighton and Talal G. Shamoon. “Secure Spread Spectrum Watermarking for multimedia”. ICIP’97, vol. 6, pp. 1673-1687, Santa Barbara, California, USA, October 1997.

[3]

X. Xia, C. Boncelet, and G. Arce, “A Multiresolution Watermark for Digital Images”. Proc. IEEE Int. Conf. on Image Processing, Oct. 1997, vol. I, pp. 548-551.

[4]

F. Bartolini, M. Barni, V. Cappellini, and A. Piva, “Mask Building for Perceptually Hiding Frequency Embedded Watermarks,” Proc. Int. Conf. on Image Processing, Oct. 1998, vol. I, pp. 450-454.

[5]

D. Kundur and D. Hatzinakos, “A Robust Digital Image Watermarking Method Using Wavelet-Based Fusion,” ICIP, Oct. 1997, vol. I, pp. 544547.

[6]

J. Delaigle, C. De Vleeschouwer, and B. Macq, “Psychovisual Approach to Digital Picture Watermarking,” Journal of Electronic Imaging, vol. 7, no. 3, pp. 628-640, July 1998.

[7]

Reza Safabakhsh, Shiva Zaboli, and Arash Tabibiazar, “Digital Watermarking on Still Images Using Wavelet Transform”, IEEE International Conference on Information Technology: Coding and Computing (ITCC’04), vol. 1, p.p.671-675, Las Vegas, Nevada, USA, April 5-7.

[8]

P. Bas, J. Chassery, and F. Davoine, “Using the Fractal Code to Watermark Images,” Proc. IEEE Int. Conf. on Image Processing, vol. I, Oct. 1998, pp. 469-473.

[9]

E. J. Cand`es and D. L. Donoho, “Curvelets – a surprisingly effective nonadaptive representation for objects with edges,” in Curve and Surface Fitting, A. Cohen, C. Rabut, and L. L. Schumaker, Eds., Saint-Malo, 1999, Vanderbilt University Press.

0.3 0.2 0.1

CEW Method I Method II

0 Baboon

Gray

Sat

City Image Name

Lena

Pepper

Barbara

Fig 12. Maximum Similarity of some image under Gaussian Attack Max Similarity under Rotation attack 1 0.9 0.8

Maximum Similarity

0.7 0.6 0.5 0.4 0.3 0.2 0.1

CEW Method I Method II

0 Baboon

Gray

Sat

City Image Name

Lena

Pepper

Barbara

Fig 13. Maximum Similarity of some image under Gaussian Attack

V.

CONCLUSIONS

We described a unified approach to digital watermarking of images using Contourlet transform. Adding a scrambled watermark to high-pass coefficients in an adaptive way based on entropy allows high performance detection in watermark extraction. Robustness of the CEW algorithm is evaluated under several attacks. The method shows a better performance in watermark detection than the current well-known schemes. By increasing the levels of decompositions, the watermarking capacity is increased and the quality of extracted watermark is improved.

[10] Jayalakshmi M., S. N. Merchant, Uday B. Desai, “Digital Watermarking in Contourlet Domain”, The 18th International Conference on Pattern Recognition (ICPR'06), vol. 3, p.p. 861-864, Hong Kong, August, 20-24. [11] Do, M. N., and Vetterli, M., “Contourlets: a directional multiresolution image representation,” in Proc. ICIP, vol. 1, 2002, pp. 357-360. [12] N. Baaziz, “Adaptive watermarking schemes based on a redundant contourlet transform,” Proc. ICIP, Genoa, Italy, September 2005.

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