A robust color image watermarking with Singular Value Decomposition method

A robust color image watermarking with Singular Value Decomposition method

Advances in Engineering Software 42 (2011) 336–346 Contents lists available at ScienceDirect Advances in Engineering Software journal homepage: www...

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Advances in Engineering Software 42 (2011) 336–346

Contents lists available at ScienceDirect

Advances in Engineering Software journal homepage: www.elsevier.com/locate/advengsoft

A robust color image watermarking with Singular Value Decomposition method Sengul Dogan a,⇑, Turker Tuncer a, Engin Avci a, Arif Gulten b a b

Firat University, Department of Electronic and Computer Science, 23119 Elazig, Turkey Firat University, Department of Electrical and Electronic Engineering, 23119 Elazig, Turkey

a r t i c l e

i n f o

Article history: Received 28 January 2011 Received in revised form 24 February 2011 Accepted 24 February 2011 Available online 3 April 2011 Keywords: Watermarking Multi-modal biometric Singular Value Decomposition (SVD) Color image Data hiding Data security

a b s t r a c t The performance of a watermarking method based on Singular Value Decomposition (SVD) has been improved for color image in this paper. One of the common methods used for hiding information on image files is Singular Value Decomposition method which used in the frequency domain. In Singular Value Decomposition based watermarking techniques; watermark embedding can usually be achieved by modifying the least significant bits of the singular value matrix. This paper gives application results which show the watermarking security of using this algorithm for the watermarking and demonstrate the accuracy of these methods. The performance comparison of the algorithms was also realized. Ó 2011 Elsevier Ltd. All rights reserved.

1. Introduction Biometrics is the ability to authenticate the true identity of an individual [1]. Establishing the authenticity of biometric data has emerged as an important research issue with the wide spread utilization of biometric identification systems. The fact that biometric data is not replaceable and is not secret combined with the existence of several types of attacks that are possible in a biometric system, makes the issue of security/integrity of biometric data extremely critical [2]. At the same time, it has been shown that some people have difficulty with fingerprint for enrollment or verification due to the inherent characteristics of their finger [3,4]. Sometimes the problem is associated with noisy data from biometric sensors and environmental conditions. All these lead to an increase in false acceptance and false rejection rates [5–7]. To overcome these problems, multi-modal biometrics relies on more than one form of biometric data. An overview of the multi-modal biometric algorithms is presented in. Biometric systems are divided into two basic physical and behavioral [8]. When designing a biometric system, one or more features of these methods can be used. The general physical and behavioral features used in biometric are listed below: 1. Facial features. 2. Ocular features. ⇑ Corresponding author. Tel.: +90 424 2370000 4350. E-mail address: [email protected] (S. Dogan). 0965-9978/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.advengsoft.2011.02.012

3. 4. 5. 6.

_  Iris.  Retina. Fingerprint. Hand geometry. Audio features. Handwriting and signature [9].

Geometric attacks such as blurring, sharpening, rotation and scaling are easy to apply and may lead many watermark detectors to total failure due to loss of synchronization between the embedded and the correlating watermarking. Several watermarking methods tough geometric attacks have been presented in literature [10,11]. In this study, multi model biometric system based DCT has been developed and face images and iris images of same person are combined for creating a reliable multi model biometric system. This paper is organized as follows. Section 2 describes watermarking technique. Section 3 gives Singular Value Decomposition (SVD) method. Section 4 contains applications of the proposed system and experimental results. Finally, the results discussed in Section 5. 2. Watermarking Watermarking is a technique that specified data are embedded in a digital content as reliable and robust [12]. The embedding takes place by manipulating the content of the digital data, which means the information is not embedded in the frame around the data. However, it is required that whether the watermark is

S. Dogan et al. / Advances in Engineering Software 42 (2011) 336–346

Fig. 1. The block diagram of watermarking method.

Table 1 The most widely used watermarking methods. Criteria

Variety

Document type Human perception Workspace Logo Variety information

Image, video, audio, text Visible, invisible Spatial, conversion PRN, visible water Non-blind, half-blind, blind

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embedded or not be almost unrecognizable. Another very important requirement is its robustness against attacks to remove or modify the embedded watermark: the embedded watermark should not be removed or seriously modified by various malicious or un-malicious attacks as long as the original content is useful [13–16]. The block diagram of watermarking method is given below in Fig. 1. There are different classifications of invisible watermarking algorithms. The reason behind this is the enormous diversity of watermarking schemes. Watermarking approaches can be distinguished in terms of watermarking host signal (still images, video signal, audio signal, integrated circuit design), and the availability of original signal during extraction (non-blind, semi-blind, blind). The classifies of watermarking method can be given as in Table 1. Also, they can be categorized based on the domain used for watermarking embedding process, as shown in Fig. 2. The watermarking application is considered one of the criteria for watermarking classification. Fig. 3 shows the subcategories based on watermarking applications [17–19]. 3. Singular Value Decomposition (SVD) SVD is extensively used in data processing and visualization. Applied to a positive matrix, the regular additive SVD by the first several dual vectors can yield irrelevant negative elements of the approximated matrix. An A matrix can be decomposed multiplied of three different matrix with SVD method [20].

Fig. 2. Classification of watermarking algorithms based on domain used for the watermarking embedding process.

Fig. 3. Classification of watermarking technology based on applications.

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Fig. 4. The block diagram of watermark adding processing of the system based on SVD.



r X

U i Si V Ti

Fig. 5. The block diagram of watermark extraction processing.

i¼0

2

u1;1

6 u2;1 6 A ¼ USV T ¼ 6 6 .. 4 .

32

r1

0

6 u2;N 7 76 76 .. 76 . 54

0 .. .

r2   

u1;N

 

0 3T

uN;N

uN;1 2

v 1;1 6 v 2;1 6

6 6 .. 4 .



.. .

..

0



.

0

3

0 7 7 7 7 0 5

rN

v 1;N v 2;N 77  

v N;1

7 .. 7 . 5

v N;N

where U and V are N  N orthogonal matrices and S is an N by N singular, diagonal matrix with diagonal entries satisfying r1 P r2 P    P rr P rrþ1 ¼    ¼ rN ¼ 0 [21]. There are three main properties of SVD from the viewpoint of image processing applications: (1) The singular values of an image have very good stability, that is, when a small (Perturbation is added to an image, singular values do not change significantly) [22]. (2) Each singular value specifies the luminance of an image layer while the corresponding pair of singular vectors specifies the geometry of the image [23].

Table 2 The PSNR results of the developed watermarking system based SVD method for multi model biometric color face and iris images.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Image original image (896  592) watermarking image (50  38)

Obtained PSNR of normal watermarked image

Obtained PSNR of sharpen image

Obtained PSNR of blurred image

Obtained PSNR of turned (60°) image

Obtained PSNR of scaled (0.5) image

Watermarking Watermarking Watermarking Watermarking Watermarking Watermarking Watermarking Watermarking Watermarking Watermarking Watermarking Watermarking Watermarking Watermarking Watermarking Watermarking Watermarking Watermarking Watermarking Watermarking

63.6430 62.1307 62.1400 62.0540 62.0530 62.1551 62.2417 62.2779 62.2271 62.2028 62.3181 62.1359 62.2133 62.2170 62.0659 62.0942 62.1723 62.0646 62.0918 62.0915

57.0607 53.7329 53.0400 53.4381 53.4634 53.3003 53.0961 52.1467 52.9779 52.8169 54.1515 53.1591 53.3332 52.9179 53.2953 53.3746 52.6161 53.0208 53.6906 53.5920

68.9082 67.7336 67.5707 68.0171 67.9173 67.6089 67.5948 68.4574 67.9335 68.4437 67.7885 67.9920 67.7791 68.1040 67.6813 68.2260 68.1542 67.1380 68.0666 67.0680

69.6448 67.2702 66.8951 66.9537 66.8334 66.8723 66.4707 66.6246 66.5371 66.6422 67.4227 66.6442 66.9757 66.4494 66.7455 66.7509 66.4280 66.5983 66.8312 66.9744

67.0938 64.6304 64.2582 64.3141 64.2363 64.3045 63.9379 64.1321 64.0175 64.2649 64.7315 64.0729 64.2545 64.0455 64.2609 64.2399 63.9308 63.9063 64.2752 64.1876

image image image image image image image image image image image image image image image image image image image image

(man 1-iris 1) (man 2-iris 2) (man 3-iris 3) (man 4-iris 4) (man 5-iris 5) (man 6-iris 6) (man 7-iris 7) (man 8-iris 8) (man 9-iris 9) (man 10-iris 10) (woman 1-iris 11) (woman 2-iris 12) (woman 3-iris 13) (woman 4-iris 14) (woman 5-iris 15) (woman 6-iris 16) (woman 7-iris 17) (woman 8-iris 18) (woman 9-iris 19) (woman 10-iris 20)

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(3) Singular values properties.

represent

intrinsic

algebraic

image

A theoretical analysis of the effects of ordinary geometric distortions on the singular values of an image is provided in a recent paper [24].

Fig. 6. Original watermark.

Fig. 8b. The histogram curve for G mode of original image. Fig. 7. Extracted watermark from original image.

Fig. 8. Original image.

Fig. 8c. The histogram curve for B mode of original image.

Fig. 8a. The histogram curve for R mode of original image.

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Fig. 9. Watermarked image PSNR: 63.6430.

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4. The application of watermarking method based SVD method for multi model biometric color face and iris images In this application study, the watermarking system based Singular Value Decomposition (SVD) for multi model biometric color face and iris images are developed. This system has very high Peak

Fig. 9a. The histogram curve for R mode of watermarked image.

Fig. 10. Sharpen original image.

Fig. 9b. The histogram curve for G mode of watermarked image.

Fig. 10a. The histogram curve for R mode of sharpen original image.

Fig. 9c. The histogram curve for B mode of watermarked image.

Fig. 10b. The histogram curve for G mode of sharpen original image.

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Fig. 10c. The histogram curve for B mode of sharpen original image.

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Fig. 11b. The histogram curve for G mode of sharpen watermarked image.

Fig. 11. Sharpen watermarked image PSNR: 57.0607.

Fig. 11c. The histogram curve for B mode of sharpen watermarked image.

Fig. 12. Blurred original image. Fig. 11a. The histogram curve for R mode of sharpen watermarked image.

Signal to Noise Ratios (PSNRs). This status shows that this developed method is a valid method for multi model biometric systems. In this study, expert multi model biometric system based SVD has been developed. For this purpose, the database is created by using both face images and iris images of 100 people. The face image of each of people is divided into three sub-matrix

P (A ¼ ri¼0 U i Si V Ti ) by using Singular Value Decomposition (SVD) method. Then, S that contains singular value of the image, matrix of each of face images and iris images of same people are converted to binary codes. Thus, the binary codes of each of iris images are embedded into S matrix of each of face images by using Least Significant Bit Insertion (LSB) method. The block diagram of this developed watermarking system based Singular Value Decomposi-

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Fig. 13. Blurred watermarked image PSNR: 68.9082.

Fig. 12a. The histogram curve for R mode of blurred original image.

Fig. 13a. The histogram curve for R mode of blurred watermarked image.

Fig. 12b. The histogram curve for G mode of blurred original image.

Fig. 13b. The histogram curve for G mode of blurred watermarked image.

Fig. 12c. The histogram curve for B mode of blurred original image.

tion (SVD) for multi model biometric color images can be given as below in Fig. 4.

PSNR values are calculated according to the following equation in the system.

PSNR ¼ 10  log10

1 NN

PN PN i¼1

!

2552

j¼1 ½Ii ði; jÞ

 I2 ði; jÞ2

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Fig. 13c. The histogram curve for B mode of blurred watermarked image. Fig. 14b. The histogram curve for G mode of turned original image.

Fig. 14. Turned original image (60°).

Fig. 14c. The histogram curve for B mode of turned original image.

Fig. 14a. The histogram curve for R mode of turned original image. Fig. 15. Turned watermarked image (60°) PSNR: 69.6448.

The block diagram of the system for watermark extract can be given as below in Fig. 5. The developed watermarking system based SVD method for multi model biometric color images is attempted for total 100

people. The watermarked images are applied to four different attacks (blurring, sharpening, image rotation and image scaling). Averaged Peak Signal to Noise Ratios (PSNRs) for these water-

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Fig. 16. Scaled original image (0.5).

Fig. 15a. The histogram curve for R mode of turned watermarked image.

Fig. 16a. The histogram curve for R mode of scaled original image.

Fig. 15b. The histogram curve for G mode of turned watermarked image.

Fig. 16b. The histogram curve for G mode of scaled original image.

Fig. 15c. The histogram curve for B mode of turned watermarked image.

marked multi model biometric color images of 100 people are calculated about 62.71229. Invisible images of the watermarking must have three basic features: transparency, high durability and the capacity to attack. Four different attacks, which are blurring, sharpening, image rotation, image scaling, were applied to watermarked multi model biometric color images on our system. These

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Fig. 16c. The histogram curve for B mode of scaled original image.

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Fig. 17b. The histogram curve for G mode of scaled watermarked image.

Fig. 17. Scaled watermarked image (0.5) PSNR: 67.0938.

Fig. 17c. The histogram curve for B mode of scaled watermarked image.

for 20 of total 100 persons. The processes steps mentioned above are showed on sample Figs. 6–17 in below respectively. 5. Results

Fig. 17a. The histogram curve for R mode of scaled watermarked image.

attacks led to declines in rates of PSNR. If optimum scaling values of developed watermarking system based SVD is selected, more successful PSNR results can be obtained. The results are given in the Table 2. In this table the obtained PSNR results are showed

In this paper, we propose a watermarking scheme in the spatial domain for color image using SVD technique. A reliable biometric system requires at least two biometrics features such as face images, iris images, fingerprint images, and speech signals of same person. In this study, for this reason, face images and iris images of same person are combined for creating the reliable multi model biometric systems. In contrast with past approaches of watermarking, our method is that three different dimension of color iris image are embedded into color face image for data hiding. So it is increased the amount of hidden information. Also SVD method belongs to spatial domain transform and has robustness to geometrical attack. The developed watermarking system based SVD method for multi model biometric color face and iris images is attempted for total 100 people. Averaged PSNR ratios for these watermarked multi model biometric color face and iris images of 100 people are calculated about 62.71229. The results show that high data hiding and decoding performance for color face and iris images is also

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observed by using this developed multi model biometric system based SVD method. On the other hand, the results of the test have shown our watermarking is very imperceptible and robust especially for four different attacks, which are blurring, sharpening, image rotation, image scaling. References [1] Vatsa M, Singh R, Mitra P, Noore A. Digital watermarking based secure multimodal biometric system. IEEE Int Conf Syst Man Cybernet 2004. [2] Islam MR, Sayeed MS, Samraj A. Multimodality to improve security and privacy in fingerprint authentication system, intelligent and advanced systems. In: ICIAS 2007; November 2007, p. 753–7. [3] Noore A, Singh R, Vatsa M. Robust memory-efficient data level information fusion of multi-modal biometric images. Inform Fusion 2007;8:337–46. [4] Jain AK, Ross A, Prabhakar S. An introduction to biometric recognition. IEEE Trans Circ Syst Video Technol 2004;14(1):4–20. [5] Gutkowski P. Algorithm for retrieval and verification of personal identity using bimodal biometrics. Inform Fusion 2004;5(1):65–71. [6] Kutter M, Jordan F, Bossen F. Digital watermarking of color images using amplitude modulation. J Electron Imag 1998;7(2):326–32. [7] Richard Wildes P. Iris recognition: an emerging biometric technology. Proc IEEE 1999;85(9):1348–63. [8] Matyas SM, Staptelon J. A biometric standard for information management and security. Comput Secur 2000;19:428–41. [9] Ross A, Jain AK. Information fusion in biometrics. Pattern Recognit Lett 2003;24(13):2115–25. [10] O’Ruanaidh J, Pun T. Rotation, scale and translation invariant spread spectrum digital image watermarking. Signal Process 1998;66(3):303–17. [11] Lin C-Y, Wu M, Bloom LA, Cox IJ, Miller ML, Lui YM. Rotation, scale and translation resilient watermarking for images. IEEE Trans Image Process 2001;10(5):767–82. [12] Cox IJ, Miller ML, Bloon JA. Digital watermarking. San Francisco: Morgan Kaufmann Publishers; 2002.

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