IEEE-International Conference on Recent Trends in Information Technology, ICRTIT 2011 MIT, Anna University, Chennai. June 3-5, 2011
Non Blind Image Watermarking Based on Similarity in Contourlet Domain Mahesh Kumar N1, Manikandan T 2, Sapthagirivasan V 3 1, 2
Department of Electronics and Communication Engineering Rajalakshmi Engineering College, Anna university, Chennai, India 1 2
3
[email protected] [email protected]
Fulltime Research Scholar, Department of Biomedical Engineering SRM University, Chennai, India 3
[email protected]
Efficiency deals with the speed of the algorithms for inserting and detecting the watermark. Digital watermarking has been used in a wide variety of applications ranging from copyright protection, covert communication, transaction tracking, content integrity, authentication of multimedia data and medical applications. Watermarking techniques can be classified as spatial and transform domain techniques. Spatial domain techniques were the one available during early stages of watermarking, where watermark is embedded by just modifying pixel values of host image. The Least Significant Bit (LSB) modification is the most common spatial domain approach in which watermarking is done by modifying the least significant bit of an image [1]. Spatial domain method has good perceptual transparency but is not resistant to the attacks. Transform domain techniques embed watermark to the values of its Keywords—Digital image watermarking, contourlet transform, transform coefficients. Existing transform domain techniques non blind, similarity. usually selected for watermarking are Discrete Cosine Transform (DCT), Discrete Fourier Transform (DFT), I. INTRODUCTION Discrete Wavelet transform (DWT) [2]-[5]. These transform In the fast growing world, internet evolution has resulted in domain techniques selects regions of middle frequency to an enormous growth to multimedia applications. Multimedia embed the watermark. Among the transform domain technology services face the most important issue of techniques, the wavelet transform is the most popular one and multimedia security and copyrights management. Digital it performs better than DCT and DFT based algorithms. DWT watermarking is considered as an effective solution for has numerous advantages including space frequency copyright protection and covert communication. Digital localization, multiresolution representation and superior HVS watermarking is the process of embedding information into modelling. Wavelet transform is considered as a good tool for the digital signal in a way that is difficult to modify or remove. representation of one dimensional signal but when applied to Watermarking can be classified as image watermarking, audio two dimensional signals it is good at isolating discontinuities watermarking and video watermarking depending on the type at edge points but has limited directionality. Therefore it of data used. Watermarking systems major requirements to cannot effectively capture the contour information. To guarantee desired functionalities are perceptual transparency, overcome these disadvantages curvelet transform was payload of the watermark, robustness and efficiency. introduced. The Curvelet transform was developed in the Perceptual transparency means hiding watermark within the continuous domain but its discretization was a problem when cover image, in such a way that there is no difference between critical sampling is required. Minh Do and Martin Vetterli the watermarked image and original image. Robustness of the proposed contourlet transform (CT) [6], which is considered watermarking system is its ability to resist various signal as an improvement over curvelets. Some watermarking processing attacks on the carrier. Some of the common signal algorithms based on contourlet transforms have been proposed processing attacks that watermarking should resist are JPEG so far [7]-[10]. Xiao et al. [7] proposed an adaptive compression, filtering, rescaling, cropping, geometric watermarking scheme based on multiscale and directionality distortions and additive noise. Payload of the watermark is the property of CT, which use the texture and luminance features amount of information that can be hidden in the cover image. Abstract - This paper presents a non blind watermarking technique for gray scale images in contourlet domain. It uses the similarity based approach to create an effective watermarking system. In the proposed scheme host image is decomposed in contourlet domain upto four hierarchical levels. The watermark logo image and the directional subband of host image are divided into image blocks of size 8x8. The similarity coefficients are calculated between watermark image block and subband image block. The watermark image block is embedded into the directional subband block with best similarity. The PSNR value obtained using the proposed method is 46.76 dB. To test the robustness of the proposed method general geometric attacks such as salt & pepper noise, poisson noise, speckle noise, cropping attacks and JPEG compression attacks were applied. The experimental result shows that the proposed method has higher robustness for cropping and JPEG compression attacks.
978-1-4577-0590-8/11/$26.00 ©2011 IEEE
1277
IEEE-ICRTIT 2011 of the host image to find the watermark embedding position. Jayalakshmi et al. [8] proposed a blind watermarking algorithm in contourlet transform which uses spread spectrum technique to embed the watermark. The watermark used is a binary logo, which is retrieved using correlation detector. Akhaee et al. [9] presented an improved multiplicative image watermarking system where watermark is embedded into highest energy directional subband. At the receiver an optimum detector based on Maximum Likelihood decision rule is used to extract the watermark. The rest of the section is organized as follows. Section 2 gives the brief introduction to contourlet transform. The proposed watermark embedding and extraction algorithm is described in section 3. Section 4 deals the experimental results and section 5 is the conclusion. II. CONTOURLET TRANSFORM Contourlet Transform introduced by Minh N. Do and Vetterli [6] is a new image representation and decomposition scheme. Contourlet transform represent images containing contours and textures very efficiently as it is a geometrical image based transform. Contourlets posses the critical sampling, multiresolution and localization properties of wavelets in addition to directionality and anisotropy, that makes contourlets superior than other transform. The directionality property means having basis function at many directions compared to only three directions of wavelets where as the anisotropy property means that the bases functions appear at various aspect ratios. Contourlet is a double filter bank structure containing two major stages, the subband decomposition and directional transform. In the first stage Laplacian Pyramid (LP) is used to capture point discontinuities followed by Directional Filter Bank (DFB) in the second stage to link these point discontinuities into linear structure. Combining the Laplacian pyramid and the directional Filter Bank into a double filter bank structure, the contourlet transform is developed. Contourlet filter bank is represented in figure 1.
generates only one bandpass image which does not have scrambled frequencies. Directional decomposition stage is implemented using Directional Filter Bank developed by Bamberger and smith [12]. DFB processes the bandpass image obtained from LP decomposition. Its construction involves modulating the input signal and using diamond shaped filters to obtain the desired frequency partition. DFB is efficiently implemented via n-level tree structured decomposition that leads to 2n subbands with wedge shaped frequency partition as shown in figure 2.
Fig. 2 DFB Frequency partitioning wedge shaped frequency subbands where n=3 and 23=8 [6]
III. PROPOSED METHOD The proposed method has two stages they are watermark embedding and watermark extraction. A. Watermark Embedding In the embedding process the host image is decomposed into directional subbands. The watermark image is embedded into the higher directional subband, based on the block similarity approach [13]. The various steps involved in watermark embedding are shown in figure 3. Steps of embedding algorithm are as follows: 1) Host image of size 512x512 undergoes four hierarchical levels of contourlet decomposition to produce 16 directional subbands. At each level there are 2n directional subbands, where n=1, 2, 3, 4. 2) Divide the watermark image W ( x, y ) and 15th directional
subband D ( x, y ) into blocks of size 8x8 respectively. Watermark image block is denoted as W ( x, y ),1 ≤ x, y ≤ 8,1 ≤ i, j ≤ 8 and directional subband i, j Fig. 1 Contourlet Filter Bank [10]
Burt and Adelson introduced Laplacian pyramid [11], a method for achieving multiscale decomposition. The function of LP is to decompose the image to generate a sampled low pass version of the original and the difference between the original and the prediction resulting in a bandpass image. The distinguishing feature that LP has is that each pyramid level
block as Di' , j' (x, y),1 ≤ x, y ≤ 8,1 ≤ i' ≤ 32,1 ≤ j' ≤ 8 . Here the watermark image is of size 64x64 and the directional subband is of size 256x64.
3) Generate row and column matrices to record the row and column indexes of the host image block with best similarity coefficients, flag matrix F is used to identify if a
1278
Non Blind Image Watermarking Based on Similarity in Contourlet Domain
directional subband block is embedded with watermark image block.
4) Compute the similarity coefficients between the watermark image block W ( x, y ) and directional subband i, j image block D (x, y ) with the following formula: i' , j'
(
∑ ∑ A ( x, y )A ( x, y ) 2 x y 1
)
S A1 , A2 =
∑∑ A x y 1
2
(x, y )
∑∑A x y 2
2
(x, y )
(1)
W ( x, y ) = (D w ( x, y ) − (1 − α )D ( x, y )) / α
Wi , j is the watermark image block and Di , j is the
directional subband blocks.
5) When the directional subband block has the best similarity with watermark image block, embed the watermark block using the formula D w (x , y ) = (1 − α )D ( x, y ) + αW ( x,. y ) Where
1) Perform contourlet decomposition on the host image and watermarked image to produce the directional subbands. 2) Find the embedded watermark image blocks by using row and column index matrices R and C. Each watermark image block can be recovered from the 15th directional subband by using following formula
A1 = Wi, j , A2 = Di, j Where
B. Watermark Extraction The watermark extraction algorithm is a non blind method where it makes use of original watermark image blocks as reference. The watermark image can be recovered from watermarked image by using the row index matrixes R and column index matrixes C. The extraction algorithm is shown in figure 4. Steps involved in extracting the watermark are as follows.
( )
D w x, y
(3)
3) Repeat the above step, till the entire watermark image is recovered.
(2)
represents the coefficients of the
embedded directional subband and
α
is the strength factor.
6) Take the next watermark image block and repeat step 4and step 5, until the entire watermark image blocks are embedded. 7) After all the watermark image block is embedded into directional subband, Inverse contourlet Transform (ICT) is performed to obtain the watermarked image.
Fig. 4 Watermark extraction flow diagram
Fig. 3 Watermark embedding flow diagram
IV. EXPERIMENTAL RESULTS In order to validate the performance of the proposed watermarking technique, we conducted tests on different kinds of images and introduced various attacks to test its robustness. The experiments were held on number of standard test images. The host image used is Lena of size 512x512. Watermark is gray scale face and gray scale tomb of size 64x64. As a first step host image is decomposed using contourlet transform, four levels of decomposition is performed to produce sixteen directional subbands. Among these sixteen directional subbands, we have chosen 15th directional subband for embedding. The watermark image and the 15th directional
1279
IEEE-ICRTIT 2011 robustness. The host Lena image, watermark face image, watermarked image and extracted watermark is shown in fig. 5.
subband image are divided into 8 blocks of size 8x8. Then each watermark image block is embedded into subband image block based on its similarity. We have set the strength factor α value to 0.1 to obtain trade off among perceptibility and
(a)
(b)
(c)
(d)
Fig. 5 (a) Host lena image (b) Watermark face image (c) Watermarked image with PSNR 47.38 dB (d) Extracted watermark with NC =0.98
(a)
(b)
(c)
(d)
Fig. 6 (a) Host image (b) Watermark tomb image (c) Watermarked image with PSNR 46.86 dB (d) Extracted watermark with NC=0.99
(a)
(b)
(c)
(d)
(e)
Fig. 7 Watermarked image attacked with (a) Salt & pepper noise with noise density 0.001 (b) Poisson noise (c) Speckle noise with noise density 0.001 (d) Cropping attack with crop size 256x256 (e) JPEG compression with quality factor 85
(a)
(b)
(c)
(d)
(e)
Fig. 8 (a) watermark extracted from salt & pepper noise attack with NC 0.95 (b) watermark extracted from poisson noise attack with NC 0.71 (c) watermark extracted from speckle noise attack with NC 0.93 (d) watermark extracted from cropping attack with NC 0.98 (e) watermark extracted from JPEG compression attack with NC 0.97
The proposed watermarking method for the host lena image and watermark tomb image is shown in figure 6. To access the robustness of our watermarking algorithm cropping attack and
general geometric attacks like salt & pepper noise with noise density 0.001, poisson noise, speckle noise with noise density 0.001 and JPEG compression with quality factor 85 were
1280
Non Blind Image Watermarking Based on Similarity in Contourlet Domain
applied. The watermarked image attacked with various noises like salt & pepper, poisson, speckle, cropping and JPEG compression is shown in figure 7. The watermarks extracted from various noises are shown in figure 8. The parameters such as Peak Signal to Noise Ratio (PSNR) and Normalized Correlation (NC) are used to measure the quality of embedded watermark and extracted watermark. The PSNR and NC are calculated as follows. ⎡ 255 2 ⎤ (4) PSNR = 10 log10 ⎢ ⎥ (dB ) ⎣⎢ MSE ⎦⎥
with other methods. It shows that the PSNR value of the proposed method is better than the other methods. TABLE 1 PSNR COMPARISON OF PROPOSED METHOD WITH DIFFERENT METHOD
Image
x
Where MSE is the mean squared error and is given by 2 1 MSE = I i , j − I ' i , j (dB ) (5) ∑iM ∑N = 1 j = 1 M N Where I i , j the pixel value of the host image, and I ' i , j is
(
)
Wang’s method
Ghannam’s method
Lena
[15] 38.2
[14] 41.26
Peppers
38.7
41.25
46.82
46.39
Baboon
41.26
45.51
45.99
Barbara
Not Availabl e (NA) NA
NA
46.39
Goldhill
39.8
41.24
NA
46.87 46.68
One of our earlier method [16] 46.35
Proposed method 46.86
the pixel value of the watermarked image. M and N are the width and height of the host image respectively. m n ∑ ∑ W (i , j )W ' (i , j ) i =1 j =1 NC (W , W ') = (6) m n 2 m n 2 ∑ ∑ W (i , j ) ∑ ∑ W ' (i , j ) i =1 j =1 i =1 j =1
Where W (i, j ) are the original watermark, W ' (i, j ) are the extracted watermark, m and n are width and height of watermark respectively. Table 1 shows the PSNR comparison of the various images using the proposed method with other methods such as Ghannam’s method [14] and wang’s method [15]. The average PSNR value obtained by the proposed method is 46.55 dB. However the average PSNR value of Wang’s method and Ghannam’s method was 38.9 dB and 41.25 dB. Figure 9 shows the PSNR comparison of proposed method
Fig. 9 PSNR Comparison of proposed method with other methods
TABLE 2 ROBUSTNESS OF PROPOSED METHOD
Attacks Images
Salt & pepper noise 0.001 noise density
Speckle noise 0.001 noise density
Cropping of size 256x256
Poisson noise
JPEG Compression with quality factor 85
NC 1
NC 2
NC 1
NC 2
NC 1
NC 2
NC 1
NC 2
NC 1
NC 2
Living room
0.92
0.93
0.93
0.92
0.97
0.94
0.73
0.71
0.97
0.96
Lena
0.92
0.93
0.94
0.92
0.98
0.97
0.73
0.71
0.97
0.97
Baboon
0.91
0.89
0.92
0.90
0.95
0.93
0.75
0.73
0.94
0.94
Barbara
0.92
0.91
0.94
0.93
0.98
0.98
0.74
0.71
0.97
0.97
Peppers
0.91
0.90
0.93
0.89
0.96
0.92
0.73
0.70
0.96
0.93
Goldhill
0.92
0.92
0.91
0.89
0.98
0.97
0.73
0.70
0.97
0.97
1281
IEEE-ICRTIT 2011 Robustness of proposed method against various attacks is shown in Table 2. The various images used for the experiment are living room, lena, baboon, barbara, peppers, airplane and goldhill. We have used two watermarks to test the robustness. One is the gray scale tomb of size 64x64 and the other is the gray scale face of size 64x64, obtained from face database. NC 1 denotes the normalized correlation values of the extracted tomb watermark for various test images. NC 2 is the normalized correlation values of the extracted face watermark. Figure 10 shows the proposed methods robustness against various attacks. From the figure one can see that this method has higher robustness for cropping and JPEG compression attacks.
against salt and pepper noise, speckle noise, poisson noise, cropping and JPEG compression attacks. In future the embedding may be done in other subbands to improve the resistance against various attacks and to improve the quality of extracted watermark. ACKNOWLEDGEMENT The authors express their heartfelt gratitude to the staff members in the department of electronics and communication engineering, Rajalakshmi Engineering College, Chennai and sincere gratitude to the faculty members of Biomedical Engineering, SRM University for their grateful help. REFERENCES [1] [2] [3]
[4] [5]
[6] Fig. 10 The proposed methods robustness against various attacks
[7]
V. CONCLUSION In this paper we discussed a non blind watermarking method in contourlet domain based on block similarity approach. In the proposed method, a gray scale watermark was embedded into the 15th directional subband based on block similarity. We have chosen higher frequency subband for embedding because Human Visual System (HVS) is less sensitive to the changes made in the high frequency bands. The watermarked image has good perceptual quality with average PSNR value of (46.55 dB), which is higher than the earlier methods used for watermarking shown in table 1. That is in the proposed study, the value of PSNR increased were 18.5%, 12% and 1.1% when comparing to Wang’s method [15], Ghannam’s method [14] and one of our earlier contourlet based watermarking method [16] respectively. The extracted watermark by conventional methods [14], [15] shows less robustness against cropping attack, whereas our earlier method [16] shows better robustness for cropping attack and the NC value obtained was 0.92 averagely and the proposed method has better NC value of 0.98. In our earlier method we were applied scrambling technique before embedding watermark. By increasing the levels of decomposition we were able to increase the resistance against various attacks and improve the quality of the extracted watermark image. The experimental result shows that the proposed method is robust
[8]
[9] [10] [11] [12] [13] [14] [15] [16]
1282
V. Sapthagirivasan, “Watermarking for medical images”, unpublished M.E thesis, Anna University, Chennai, 2007. A. Piva, M. Barni, F. Bartolini, and V. Cappellini, “DCT-Based watermark recovering without restoring to the uncorrupted original image, Proc. IEEE Int. conf. Image Processing, 1997. V. Solachidis and I. Pitas, “Optimal detector for multiplicative watermarks embedded in the DFT domain of non-white signals,” EURASIP J. Appl. Signal Process., vol. 2004, no. 16, pp. 2522–2532, 2004. S. H. Wang and Y. P. Lin, “Wavelet tree quantization for copyright protection watermarking,” IEEE Trans. Image Process., vol. 13, no. 2, pp. 154–165, Feb. 2004. D. N. Vizireanu and R. O. Preda, “A New Digital Watermarking Scheme for Image Copyright Protection using Wavelet Packets”, IEEE Int. Conf. Telecom. in Modern satellite and Broadcast services, vol. 2, pp. 518-521, 2005. M. N. Do and M. Vetterli, “The contourlet transform: An efficient directional multiresolution image representation,” IEEE Trans. Image Process., vol. 14, no. 12, pp. 2091–2106, Dec. 2005. S. Xiao, H. Ling, F. Zou and Z. Lu, “Adaptive Image Watermarking Algorithm in Contourlet Domain”, Frontier of Computer Science and Technology, FCST, Japan-China Joint Workshop, pp. 138-141, April 2007. M. Jayalakshmi, S. N. Merchant and U. B. Desai, “Blind watermarking in contourlet domain with improved detection”, IEEE Int. Conf. Intelligent Information Hiding Multimedia signal processing, pp. 449452, 2006. M. A. Akhaee, S. M. E. Sahraeian, and F. Marvasti, “Contourlet based image watermarking using optimum detector in a noisy environment,” IEEE Trans. Image Process., vol. 19, no. 4, pp. 967-980, April 2010. B. C. Mohan, S. S. Kumar, "Robust Digital Watermarking Scheme Using Contourlet Transform" International Journal of Computer Science and Network Security, vol. 8, no.2, February 2008. P. J. Burt and E. H. Adelson, “The Laplacian pyramid as a compact image code,” IEEE Trans. Comm., vol. 31, no. 4, pp. 532–540, April 1983. R. H. Bamberger and M. J. T. Smith, “A filter bank for the directional decomposition of images: Theory and design,” IEEE Trans. Signal Process., vol. 40, no. 4, pp. 882–893, April 1992. H. Liao and R. Ye, “A novel digital image watermarking approach based on image blocks similarity”, IEEE Int. Conf. Image and Signal process. vol. 5, pp. 626-630, 2008. S. Ghannam and F. E. Z Abou-Chadi, “Enhancing robustness of digital image watermarks using contourlet transform”, IEEE ICIP, pp. 36453648, 2009. S. H. Wang, Y. P. Lin, “Wavelet tree quantization for copyright protection watermarking”, IEEE Trans. Image Process., vol. 13, no. 2, pp. 154-165, February 2004. N. Maheshkumar, T. Manikandan and V. Sapthagirivasan, “Non blind image watermarking using contourlet transform”, Indian journal of computer science and engineering, vol.2, no.1, pp. 31-38, 2011.
