A Robust Watermark Scheme for Copyright Protection

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A Robust Watermark Scheme for Copyright Protection Jung-Chun Liu 1, Chu-Hsing Lin 1, Wei-Shen Lai 2, Yan-Wei Lee 1 1 Department of Computer Science and Information Engineering, Tunghai University, Taiwan 2 Department of Information Management, Chienkuo Technology University, Taiwan 1 {chlin, jcliu, g95280063 }@thu.edu.tw 2

[email protected] Abstract

In this paper, we propose a robust watermark technology to protect copyright. We enhance robustness of the watermark such that it can be used to protect the intellectual property even after serious attacks on the watermarked image. In the proposed watermarking, we embed watermarks by Singular Value Decomposition and Distributed Discrete Wavelet Transformation techniques. Regarding our method, Singular Value Decomposition provides the robustness to protect the image from most attacks except cropping, while Distributed Discrete Wavelet Transformation disperses watermarks all over the image to resist cropping attacks. The robustness of our proposed watermark scheme has been verified experimentally against main kinds of watermark attacks such as geometric and signal processing attacks.

1. Introduction Digitized information and the Internet usher in the digital era. Duplication of digital media becomes so easy that one can do it without the sweat. Piracy of digital media without appropriate permissions becomes common practice. Digital watermark technology is used to protect the legal owner’s copyrights. It usually embeds in digital media invisible copyrighted watermarks, including the author signature, ownership and publication identification. The embedded watermark can be extracted later and used to resolve copyright disputes. Many image attacks have been used to sabotage the embedded watermarks. In this paper, we propose a robust watermark technology that can resist various watermark attacks by the Singular Value Decomposition (SVD) and 3-scale Distributed Discrete Wavelet Transformation (DDWT) techniques. This paper is organized as follows: Section 2 introduces Singular Value Decomposition (SVD) method and Distributed Discrete Wavelet Transformation (DDWT) method. Section 3 describes the proposed scheme. The experiment setting and experiment results are in Section 4. Conclusions are made in Section 5.

2. SVD and DDWT 2.1 Singular Value Decomposition SVD is an image processing technique based on linear algebra that can enhance the robustness of watermarks against attacks. A digital image A R M u N of a size of M u N , with M t N and R is the real number, can be represented by A ' s SVD defined by

A U 6V T

(1)

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Where U and V is the M u M and N u N orthogonal (unitary) matrix of A, respectively. ͂ is an M u N diagonal matrix. e.g. 6 diag (V 1 , V 2 ,..., V p ) , p min ^M , N ` and V 1 t V 2 t ... t V p t 0 which represent the singular values V i of A [1-11]. 2.2 Distributed Discrete Wavelet Transform

In 2006, Lin et al. proposed a DDWT watermarking scheme [12]. DDWT is similar to Discrete Wavelet Transformation (DWT) and consists of the horizontal process and the vertical process described below [13-14]. DDWT horizontal process

1) Separate the original image along horizontal direction into two equal blocks. 2) Add and subtract corresponding pixels on the two sub-blocks, then replace pixels on the left sub block with the result of the addition and denote pixels on the right sub-block with the result of the subtraction. Denote the processed left sub block as L and the right subblock as H. DDWT vertical process

1) Separate the horizontal processed image along vertical direction into two equal blocks. 2) Add and Subtract corresponding pixels on the two sub-blocks and replace pixels on the upper sub block with the result of the addition and pixels on the lower sub-block with the result of the subtraction. Thus, we generate four sub-blocks and denote them as LL, HL, LH, and HH shown in Fig. 1. Fig. 2 shows the result of multiple-scale DDWT. The 1-scale DDWT transform of original image with 4Ý4 pixels is shown in Fig. 3.

Figure 1. Example for labeling of the 1-scale DDWT

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Figure 2. Multiple-scale DDWT processes (a) The original image and results of (b) 1-scale DDWT (c) 2-scale DDWT (d) 3-scale DDWT

Figure 3. The 1-scale DDWT on an original image with 4×4 pixels

3. Proposed Scheme DDWT watermarking scheme proposed by Lin et al. shows robustness against cropping attacks by distributing watermark information all over the stego-image but has weakness against other attacks such as rescaling. The robustness of DDWT watermarking scheme can be enhanced by combining it with other watermarking scheme, one of such combinational scheme has been described in a paper by Lin et al. [14], in which SVD and DDWT watermarking methods embed watermarks in the full band in the frequency domain after the 3-scale DDWT. SVD method has gained a lot of attention in recent years since it shows robustness against most attacks. We offer a new approach to combine SVD and DDWT in this paper. We first applied SVD method to embed watermark WSVD on the cover image, and then perform the 3scale DDWT to embed watermark WDDWT in the frequency domain. Any one of the embedded watermarks, if retrieved successfully from the stego-image, can be used to protect copyright. There are two main parts in our proposed scheme: the watermark embedding process and the watermark extracting process [15-18]: 3.1 Watermark Embedding Process

Our digital watermark embedding process is described in the following six steps: 1) Input the original image X M uM and the watermark WN u N ; 2) Apply SVD on X and W: X

U X 6 X VXT

(2)

W

U W 6W V

(3)

T W

3) Embed the watermark by processing eigenvalues as follows:

V Yi V Xi D u V Wi

(4)

Where V Yi , V Xi , and V Wi are eigenvalues of Y, X and W, respectively. 4) Use SVD to obtain Y ' : Y ' U X 6Y VXT

(5)

5) Process Y ' with the 3-scale DDWT and embed watermarks in sub-bands LL3 and HH3

If W (i, j )

0, then Y 'LL 3 (i, j ) Y 'LL 3 (i, j ) D u 2 2 K (6)

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If W (i, j ) 1, then Y 'HH 3 (i, j ) Y 'HH 3 (i, j ) D u 2 2 K (7) 6) Apply IDDWT to obtain the stego-image Y. 3.2 Watermark Extracting Process

Our digital watermark extracting process is described in the following five steps: 1) Input the stego-image Y, the original image X, the image Y ' , and the watermark W. 2) Subtract Y ' from Y to obtain YDiff , and apply formula (8) to extract the embedded watermark WDDWT

0, if YDiff (i. j ) 0 ® otherwise ¯1,

WDDWT (i, j )

(8)

3) Apply SVD on X, Y ' and W to find their eigenvalues V X i , V Wi , and V Yi .

4) Extract 6WSVD by using

V Wi

V Wi

SVD

VY' V X D i

SVD

are elements of eigenvalues in

i

(9)

6WSVD .

5) Apply SVD to obtain WSVD:

WSVD

UW 6WSVD VWT

(10)

4. Experimental Settings and Results In our experiment, we used the original image of Lena with 24-bit RGB color and 512Ý 512 pixels shown in Figure 4 (a). The watermark is a binary image of 64Ý64 pixels shown in Figure 4 (b). The experimental environment was an HP-Compaq Presario V3016 laptop computer, with a Mobile Dual Core AMD Turion 64 X2 TL-52, 1600 MHz CPU, and 1GB RAM. We implemented the algorithm on MATLAB, running on Windows XP. Attacking tests had been done with Adobe Photo Shop.

(a)

(b)

Figure 4. (a) Original image (512×512 pixels color image) (b) Watermark (64×64 pixels binary image).

We used Peak Signal to Noise Ratios (PSNR) values to determine image quality:

PSNR

4Y[

20 log

255 dB MSE

(11)


MSE

§ 1 · m 1 m 1 2 ¨ 2 ¸ ¦¦ (D ij E ij ) m © ¹i 0 j 0

(12)

Where MSE is the mean square error of the two images. Higher values of PSNR mean that the stego-image is more similar to that of the original image. Pearson’s Correlation Coefficient is used to measure correlation between the original watermark (W ) and the extracted watermark (W ') . The Pearson’s Correlation Coefficient formula is defined as: n 1 n 1

Corr W ,W c

¦¦ W i 0 j 0

n 1 n 1

¦¦ W i 0 j 0

(a)

i, j

i, j

W

W Wci , j W c

n 1 n 1

¦¦ Wc W c 2

(13) 2

i, j

i 0 j 0

(b)

(c)

Figure 5. (a) The Stego-image Lena (PSNR = 43.616) (b) The extracted watermark WDDWT (c) The extracted watermark WSVD.

We have done a lot of image attacks experiments. To highlight the robustness of our method, we list in the following tables the results of the most interesting and standard attacks. Table 1 lists a number of stego-images after attacks together with their PSNR values These attacks are cropping 25%, rotation 15¶, Sharp 20%, Gaussian Noise 0.5%, Gaussian Blur 0.5%, contrast adjustment 40, waveform, fish eye, histogram equalization, and rescale. Table 2 lists the extracted watermark WDDWT from stego-images after attacks and their Pearson’s correlation coefficient values. Table 3 lists extracted watermark WSVD from stego-images after attacks and their Pearson’s correlation coefficient values. From Table 2, we found that the watermark embedded by using DDWT method showed strong robustness against many image attacks, including cropping, rotation, sharp, contrast adjustment, fish eye, and histogram equalization. And it showed good robustness against image attacks of Gaussian noise, Gaussian blur, and waveform. From Table 3, we found that the watermark embedded by using SVD method showed strong robustness against many image attacks, including Gaussian noise, contrast adjustment, histogram equalization. And it showed good robustness against the rescale attack. Any one of these two watermarks embedded by using SVD or DDWT techniques, if retrieved successfully, can be used to claim copyright. In this way, our proposed watermark scheme is robust against image attacks listed in Table 1.

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Table1.Attackedimages,attackedparameterandPSNRvalues Attacks (parameter) Cropping (25%)

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Attacks Images

PSNR 8.465

Rotation ( 15D )

11.427

Sharp (20)

34.593

Gaussian Noise (0.5)

42.774

Gaussian Blur (0.5)

34.308

Contrast Adjustment(40)

17.432

Waveform (Auto)

15.974

Fish Eye (Auto)

13.983

Histogram Equalization (Auto)

24.028

Rescale (512256512)

32.632


Table 2. Extracted watermark WDDWT, and its Pearson’s correlation coefficient value Attacks (parameter) Cropping (25%)

WDDWT

Corr(W,W’) 1.000

Rotation ( 15D )

0.535

Sharp (20)

0.999


0.592

Gaussian Blur (0.5)

0.729

Contrast Adjustment(4 0)

0.999

Waveform (Auto)

0.511

Fish Eye (Auto)

0.999

Histogram Equalization (Auto)

0.995

Rescale (5122565 12)

0.311

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Table3. Extracted watermark WSVD, and its Pearson’s correlation coefficient value Attacks (parameter) Cropping (25%)

WSVD

Corr(W,W’) 0.097

Rotation ( 15D )

0.864

Sharp (20)

0.621


0.999

Gaussian Blur (0.5)

0.153

Contrast Adjustment(40)

0.995

Waveform (Auto)

0.085

Fish Eye (Auto)

0.030

Histogram Equalization (Auto) Rescale (512256512)

0.967

0.764

5. Conclusions We propose a novel watermark scheme that is robust and is capable for copyright protection. To improve the requirements of security of watermarks, we successfully take advantage of the merits of DDWT and SVD watermarking techniques. The robustness of our watermark scheme has been experimentally verified that it can resist both geometry and nongeometry attacks such as cropping, rotation, sharp, Gaussian noise, Gaussian blur, contrast adjustment, waveform, fish eye, histogram equalization and rescale. Experimental results show that our scheme is robust and that it can offer copyright protection for legal owners.

6. Acknowledgement This work was supported in part by Taiwan Information Security Center, National Science Council under grants NSC-95-2218-E-001-001, NSC-95-2218-E-011-015, iCAST NSC963114-P-001-002-Y, NSC95-2221-E-029-020-MY3, and NSC 97-2221-E-029 -023.

10. References [1] H. C. Andrews and C. L. Patterson,“ Singular Value Decomposition (SVD) Image Coding,” IEEE Transactions on Communications, April 1976, pp.425-432. [2] N. Garguir, “Comparative Performance of SVD and Adaptive Cosine Transform in Coding Images,” IEEE Transactions on Communications, August 1979, pp. 1230-1234.

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[3] C. P. Soo, J. H. Chang, and J. J. Ding, “ Quaternion Matrix Singular Value Decomposition and Its Applications for Color Image Processing,” ,International Conference on Image Processing (CIP 2003), Vol.1, Sept. 14-17, 2003, pp. I-805-I-808. [4] M. Tian, S. W. Luo, and L. Z. Liao, “An Investigation into Using Singular Value Decomposition as A Method of Image Compression,” International Conference on Machine Learning and Cybernetics, Vol. 8, 18-21 Aug., 2005, pp.5200-5204 [5] R. Liu and T. Tan, “An SVD-based Watermarking Scheme for Protecting Rightful Ownership,” IEEE Transactions on Multimedia, Vol.4, Issue 1, March 2002, pp.121-128. [6] X. Tang, L. Yang, H. Yue, and Z. Yin, “A Watermarking Algorithm Based on the SVD and Hadamard Transform,” International Conference on Communications, Circuits and Systems, Vol. 2, May 27-30, 2005, pp.877. [7] S. Lee, D. Jang, and C. D. Yoo, “An SVD-Based Watermarking Method for Image Content Authentication with Improved Security,” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2005), Vol.2, March 18-23, 2005, pp.525-528. [8] K. Konstantinides and G. S. Yovanof, “Improved Compression Performance Using SVD-Based Filters for Still Images,” The Society for Imaging Science and Technology (IS&T)/ the International Society for Optical Engineering (SPIE), Vol. 2418, San Jose, CA, February 7-8, 1995, pp. 100-106. [9] R. Karkarala and P. O. Ogunbona, “Signal Analysis Using a Multiresolution Form of the Singular Value Decomposition,” IEEE Transactions on Image Processing, Vol. 10, Issue 5, May 2001, pp. 724-735. [10] M. G. Vozalis and K. G. Margaritis, “Applying SVD on Item-Based Filtering,” The 5th International Conference on Intelligent Systems Design and Applications (ISDA 2005), 8-10 Sept., 2005, pp.464-469. [11] D. V. S. Chandra, “Digital Image Watermarking Using Singular Value Decomposition,” The 45th Midwest Symposium on Circuits and Systems (MWSCAS-2002), Vol.3, Aug. 2002, pp.III-264 - III-267. [12] Chu-Hsing Lin, J. S. Jen, and L. C. Kuo, “Distributed Discrete Wavelet Transformation for Copyright Protection,” The 7th International Workshop on Image Analysis for Multimedia Interactive Services, Incheon Korea, April 19-21 2006, pp.53-56. [13] Chu-Hsing Lin, Jung-Chun Liu, and Li-Chang Kuo, “A Robust Full-Band Image Watermarking Scheme,” Proceedings of 10th International Conference of Communication Systems, Singapore, October 30, 2006. [14] Chu-Hsing Lin, Jung-Chun Liu, Li-Chang Kuo, and Jen-Chieh Chang, “Robust Multi-scale Full-Band Image Watermarking for Copyright Protection,” The 20th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE 2007),LNAI 4570, June 26-29 2007, Kyoto, Japan, pp.175183. [15] E. Ganic and A. M. Eskicioglu, “Robust DWT-SVD Domain Image Watermarking: Embedding Data in All Frequencies,” ACM Multimedia and Security Workshop 2004, Magdeburg Germany, September 20-21, 2004, pp. 166-174. [16] Ingrid Daubechies, “The Wavelet Transform: a Method for Time-Frequency Localization,” IEEE Transactions on Information Theory, Vol. 36, September 1990, pp. 961–1005. [17] V. V. F. Guzman, M. N. Miyatake, and H. M. H. Meana, “Analysis of a Wavelet-based Watermarking Algorithm,” The 14th International Conference on Electronics, Communications and Computers (CONIELECOMP 2004), 16-18 Feb. 2004, pp.283-287. [18] M. Antonini, M. Barlaud, P. Mathieu and I. Daubechies, “Image Coding Using Wavelet Transform,” IEEE Transactions on Image Processing, Vol. 1, No. 2, April 1992, pp. 205-220.

Authors Jung-Chun Liu received his B.S. degree in electrical engineering from National Taiwan University in 1990. He received M.S. and Ph.D. degrees from the Electrical and Computer Science Engineering Department at University of Texas at Austin, in 1996 and 2004, respectively. He is an assistant professor in the Computer Science Department at the Tunghai University, Taiwan. His research interests include digital signal processing, VLSI design, RF and microwave engineering, watermarking, embedded systems, and computer networks.

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Chu-Hsing Lin received both of his B.S. and M.S. degrees in applied mathematics from National Tsing Hua University and National Chung Hsing University, respectively. In 1991, he received his Ph.D. degree in computer sciences from National Tsing Hua University, Taiwan. Since then he has been a faculty of the Department of Computer Science and Information Engineering, Tunghai University. Dr. Lin is currently a professor and the chair of the CSIE department of Tunghai University. From 1995 to 1999, he has ever been the Director of the Computer Center of Tunghai. He has also been one of the Board Directors of the Chinese Information Security Association (CCISA) from 2001 till now. Dr. Lin has published over 50 papers in academic journals and international conferences. He has received over twenty project grants from government departments and private companies in recent years. In 2006, he was awarded the Outstanding Instructor Award of Master & Ph.D. Thesis by the IICM (Institute of Information & Computing Machinery). He was the winner of the 1991 Acer Long-Term Award for Ph.D. Dissertation. His current research interests include multimedia information security, wireless ad hoc networks, embedded systems applications. Wei-Shen Lai received his B.S. and M.S. degrees in computer science and information engineering from Feng Chia University and National Chiao Tung University, respectively. In 2002, he received his Ph.D. degree in computer science and information engineering from National Chiao Tung University, Taiwan. In 2004, he has been a faculty of the Department of Information Management, Chienkuo Technology University. His current research interests include network security and cryptography.

Yan-Wei Lee received the Bachelor of Science degree from the Deparment of Applied Mathmatics at Feng Chia University in 2006. He is currently a graduate student in the Computer Science Department at Tunghai Univerisity, Taiwan. His research interests include audio/video coding, watermarking, and ubiquitous computing.

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