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International Journal on Communications (IJC) Volume 3, 2014
A Robust Color Image Watermarking Based on SVD and DWT Li ZHANG*, Jia Wei XIAO, Jing Yun LUO Institute of Information Engineering, Shenzhen University, Shenzhen Guangdong 518060, China *
wzhangli@szu.edu.cn; jiaweixiao@szu.edu.cn; jingyunluo@szu.edu.cn
Abstract A robust color image watermarking based on DWT – SVD (Singular value decomposition) is proposed. Firstly, color image is transformed to YUV from RGB spatial domain. Luminance component Y is performed DWT and then singular value decomposition is applied to obtain singular values. Arnold transform and DWT are performed for the gray watermarks. Watermark is embedded into singular values, which are all from frequency bands of the last two layers. Watermark can be extracted from all attack images in the stirmark benchmark. The robustness can be reflected by experimental results in this algorithm. Keywords Digital Watermarking; SVD; DWT
Introduction Electronic products are more and more important in modern society. Dissemination and replication of digital products are getting easier. More and more people infringe electronic products in order to resist different kinds of infringements and protect the electronic products. Digital watermarking technology emerges as the times requiring. Digital watermark is a kind of copyright protection information and owners can read it out while unauthorized users can not easily remove it. Invisibility and robustness should be considered in valid watermarking algorithm. Compromises between invisibility and robustness are needed to get better results in embedding process. Feng Shi split low-frequency part of the original image into 4×4 data blocks, decomposed the singular value of each piece and calculated the largest singular value of each block. Low frequency information of watermark was embedded into these singular values. But it is not very robust to rotation attack according to experimental results. Kapre Bhagyashri S selected full band frequency to hide watermark in the three channel of YUV color space. In each level of decomposition, image was decomposed into four bands of frequencies and SVD was performed. Watermark was hidden in the singular values. Then
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ISVD and IDWT were applied to get watermarked image. However, watermark can not be extracted when strength of noise attack is higher. In this paper, a new watermarking algorithm that embeds a gray watermark into the color image is proposed based on SVD and DWT. Watermark is embedded in luminance component Y. All frequency coefficients of watermark are embedded into singular values of frequency bands, which are all from last two layers of Y. Experimental results demonstrate that the proposed watermarking has good robustness against attacks produced by stirmark benchmark. This paper is organized as follows: In section 2 the YUV color model is introduced. In section 3 digital watermark embedding and extracting algorithm are presented in detail. Experimental results of stirmark benchmark are described in section 4. Finally, in section 5 the conclusion is given. Background Color space is a three-dimensional coordinate system whose each color is indicated by a dot. Color image is transformed to YUV from RGB spatial domain. Conversions between RGB and YUV are as follows:
Y 0.299 R 0.587G 0.114 B U 0.16874 R 0.33126G 0.5B 128 V 0.5R 0.41869G 0.08131B 128
(1)
R Y 1.402(V 128) G Y 0.34414(U 128) 0.71414(V 128) B Y 1.772(U 128)
(2)
Where, Y represents luminance component. U and V mean that chrominance signal. Proposed Watermarking Algorithm In this section, the proposed watermarking algorithm including embedding and extracting process will be described in detail.
International Journal on Communications (IJC) Volume 3, 2014
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Watermark Embedding Process
Experiment Results
The steps of watermark embedding can be described as follows:
In the experiments, original cover image is a color lena with size 512×512. Visual gray watermark is with size 64×64 shown in Fig.2. Watermarked image is shown in Fig.3c. Normalized Correlation (NC) is used to measure similarity between original and extracted watermark. PSNR (peak signal-to-noise ratio) between original image and watermarked image is computed. NC of extracted and original watermark is 0.9998. PSNR of original and watermarked image is 36.0661.
Step1. Color image is transformed to YUV from RGB spatial domain to obtain the luminance component Y. Step2. DWT is performed for Y and then SVD is applied to obtain these orthogonal matrixes u1 and v1 and singular values d1. Step3. Arnold transformation and DWT are performed for watermark. All sub bands of watermark are embedded into one dimensional singular value vectors, which are all from frequency bands of the last two layers. SVD is performed to obtain these orthogonal matrixes u2, v2 and singular values d2. These singular values are applied in the following formula: A1=u1*d2*v1’ to get the embedded watermark frequency coefficients.
(a)
Original image
(b)Extracted watermark
(d)Original watermark
(c) Watermarked image
Step4.IDWT is used to get the gray watermarked image. YUV is converted into RGB color spaces to obtain color watermarked image.
FIG 2 (a) ORIGNAL IMAGE(c) WATERMARKED IMAGE (b) EXTRACTED WATERMARK (d) ORIGINAL WATERMARK
Robust Against Noise Attacks Watermark Extracting Scheme Watermark can be extracted blindly by the reverse calculation of watermark embedding. Step1. Color watermarked image is transformed to YUV from RGB spatial domain to obtain luminance component Y’.DWT is performed for Y’ to obtain all frequency bands of the last two layers. SVD is applied for these sub bands to obtain singular values.
Random dither noise attacks are from StirMark benchmark. Even though watermarked image has been accounted by noise for forty percent, watermark can also be extracted and shown in Fig.3.
Attack watermarked image Extracted watermark
Step2. These orthogonal matrixes u2 and v2, which are from the third step of the embedded process, with the singular values are applied to ISVD. The embedded watermark singular values d’ are got and key1 are loaded, then all sub bands of watermark are obtained. Step3.IDWT is applied for these sub bands to get the watermark. Watermar k
FIG 3 EXPERIMENTAL RESULTS OF NOISE 40%
DWT
Watermark embed
Arnold transform Color image
RGB to YUV
DWT
SVD Color watermarked image
FIG1. PROCESS OF WATERMRKING EMBEDDING TABLE1EXPERIMENTAL DATA OF NOISE ATTACK
Table1 shows experimental data of NC and PSNR under different intensity of noise attacks. When intensity of noise attack is from 20% to 100%, NC is from 0.9306 to 0.8477. So it is robust to noise attack.
Parameter 20% 40%
PSNR 15.3425 13.8439
NC 0.9306 0.8806
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60% 100%
International Journal on Communications (IJC) Volume 3, 2014
13.3653 12.9943
0.8627 0.8477
Attack watermarked image Extracted watermark
Robust Against Geometric Attacks Geometric attacks can be defined as all kinds of global or local affine, projective transformations and other attacks applied for watermarked image. Geometric attacks include cutting, rotation, cropping, resize, small random distortion, rotation and cutting. Watermarked image has been cut 3 blocks with size 128Ă—128, and extracted watermark is shown in Fig.4. Fig.5 shows that extracted watermark is visible, though watermarked image has been cropped in half. Attack watermarked image Extracted watermark
FIG 4 EXPERIMENTAL RESULTS OF CUTTING
Attack
watermarked image Extracted watermark
FIG 7 EXPERIMENTAL RESULTS OF SMALL RANDOM DISTORTION
Six types of geometric attacks are applied for watermarked image to evaluate the robustness of proposed scheme. Experimental data of table2 illustrate that strong robustness against geometric attacks effectively. TABLE2 EXPERIMENTAL DATA OF GEOMETRIC ATTACKS
Attacks Cutting 1/4 corner Cutting 1/2cross corner&1/4 center Cropping 25 Cropping 75 Rotation 1 degree Rotation 30 degree Rotation15&Cutting1/4 center Rotation45&Cutting1/4 center Resize 40*40 Resize 60*60 Random distortion 0.95 Random distortion 1.05
PSNR 16.8372 12.2147 15.7301 17.4620 21.6937 12.7488 12.5022 11.8980 24.2437 25.8556 19.7225 19.3920
NC 0.9982 0.9759 0.9282 0.9795 0.9855 0.9924 0.9833 0.9839 0.9624 0.9788 0.9856 0.9906
Robust Against JPEG Compression Attacks FIG 5 EXPERIMENTAL RESULTS OF CROPPING 50 Attack
watermarked image Extracted watermark
JPEG compression attack with quality factor 30 is used for watermarked image. Extracted watermark is hardly affected by this attack. When quality factors of JPEG compression attacks change, experimental data of NC and PSNR are different shown in table3. No matter how quality factor is changed, NC is similarly high. TABLE3 EXPERIMENTAL DATA OF JPEG COMPRESSION ATTACK
FIG 6 EXPERIMENTAL RESULTS OF 30°ROTATION & 1/4 CENTER CUTTING
Watermarked image has been rotated by 30 degrees and cut 1/4 from the center. Extracted watermark is shown in Fig.6. Small random distortion attack with parameter 1.1 is applied for Watermarked image. Small random distortion is defined as partial shifts, scaling and rotation attacks used for pixels of watermarked image in the absence of significant visual distortion.
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parameter 15 30 50 80 100
PSNR 28.5168 28.9367 29.0051 29.4969 29.5000
NC 0.9984 0.9993 0.9998 0.9997 0.9997
Robust Against Other Kinds of Attacks Watermarked image has been rotated by 45 degrees and cut 1/4 from the center, attack of salt &pepper noise is also applied.
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industry to develop special fund (C201005250085A, JCYJ20130329105356543).
Attack watermarked image Extracted watermark
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In the invariant condition of rotating angle and intensity of noise, NC and PSNR under different cutting parts of watermarked image are shown in the following table. Extracted watermark is similar to original watermark according to NC values. TABLE4 EXPERIMENTAL DATA OF ROTATION &SALT &PEPPER NOISE
&CUTTING ATTACK parameter Rotation45&Salt&Pepper0.3&Cutting1/ 4corner Rotation45&Salt&Pepper0.3&Cutting1/ 4 center Rotation45&Salt&Pepper0.3&Cutting1/ 2cross corner
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NC
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0.9616
Conclusions In this paper, a robust color image watermarking based on DWT—SVD is proposed, which resists image processing attacks, such as JPEG compression, noise, median filter, cropping, rotation, affine transform, random distortion and so on. Experimental results demonstrate that the proposed watermarking has a good robust against attacks produced by stirmark benchmark, especially against the geometric attacks. For example, experimental results of rotation and cutting attack show that NC is high. In the future, the enhancement of robustness against noise attack will be our main research fields.
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ACKNOWLEDGMENT
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This work is partly supported by Shenzhen Internet
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