Image Watermarking Scheme Based on DWT-DCT and SSVD

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1 pp Image Watermarking Scheme Base on DW-DC an SSVD Zhi Zhang, Chengyou Wang * an Xiao Zhou School of Mechanical, Electrical an Information Engineering, Shanong University, Weihai , China suzhi@163.com, angchengyou@su.eu.cn, zhouxiao@su.eu.cn Abstract With the fast evelopment of netork, it becomes urgent to protect copyright an rightful onership of an image, an igital atermark technology is a comprising solution to o that. Hoever, the false positive problem exists in a number of atermarking schemes base on singular value ecomposition (SVD). his paper offers a novel image atermark scheme to avoi the false positive problem, hich combines ith iscrete cosine transform (DC), iscrete avelet transform (DW), an shuffle singular value ecomposition (SSVD). In orer to aress the problem, the principal components obtaine by SSVD are embee into a constructe matrix compose of the irect current (DC) component hich is extracte from the non-overlapping image block acquire by iviing the lo frequency. he lo frequency is obtaine by applying DW to the host image. Otherise, the experiments concerning the imperceptibility an robustness are conucte in this paper. Compare ith other reliable atermark methos, the obtaine experimental results emonstrate that the propose metho has excellent imperceptibility an behaves satisfactory robustness in resisting the attacks such as Salt an Pepper noise, Gaussian noise, an image cropping, etc. Keyors: Digital Watermarking; Discrete Wavelet ransform (DW); Discrete Cosine ransform (DC); Shuffle Singular Value Decomposition (SSVD) 1. Introuction Due to the rapi evolution of netork technology, it becomes much easier for folks to get igital information an kins of resources. he Internet is greatly convenient to exchange information beteen iniviuals, but meanhile, it leas to a multitue of security problems at the aspect of copyright isputes. Facing these issues, the igital atermarking technique is a promising solution for copyright protection, hich is a technology of embeing atermark ith intellectual property rights into carriers [1] such as image, auio, an vieo. he to importance properties of robust atermarking methos are imperceptibility an robustness. he imperceptibility refers that the atermark embee in the cover image is har to be perceive by nake eyes. he robustness is efine as the ability against a host of innocent an malicious attacks such as signal processing operations an geometric attacks. Over the past several ecaes, a number of atermarking schemes have been propose an implemente by many researchers since its release. An ocean of atermarking schemes is mainly ivie into to types: (i) Spatial omain an (ii) ransform omain. It is orth mentioning that the least significant bit (LSB) [2] is one of the most representative methos in spatial omain, hich embes the atermark into the cover image by moifying the least significant bit. he transform omain inclues iscrete cosine transform (DC) [3], iscrete Fourier transform (DF), an iscrete avelet transform (DW), etc. In 2002, Liu an an [4] first propose the singular value ecomposition (SVD) base atermark scheme, hich took full avantage of the stability of SVD. Oing to its stability, a sea of hybri atermarking methos ith regar to SVD ISSN: IJSIA Copyright c 2016 SERSC

2 sprang up for a hile, for example, a atermarking scheme base DC-SVD [5], a atermarking algorithm base SVD-DW [6], an a atermarking metho base DW-DC-SVD [7, 8]. Hoever, Zhang an Li [9] state the critical efect of Liu an an s scheme, hich is also calle as false positive problem. Besies that, an attack aiming at the atermarking scheme base on SVD as presente in [10]. If there is a question, there ill be an anser. Jain et. al., [11] presente a reliable atermarking scheme base on SVD in hich the major components ere inserte into the cover image. It is base on the fact that the left an right singular matrices obtaine by SVD can store most information of a atermark. he problem has also been solve in [12]. In this paper, e propose a novel atermarking scheme base DC-DW an shuffle singular value ecomposition (SSVD) after summarizing the common point from the atermarking schemes solve the false positive problem. First of all, the host image is performe by one-level DW using Haar avelet an the lo frequency approximation sub-ban LL 1 is acquire. Meanhile, the SSVD is performe on the atermark an the principal components are chosen as the embeing information, hile the rest components are viee as a part of keys. LL 1 is ivie into non-overlapping image blocks, an then a constructe matrix is compose of the irect current (DC) components erive from the image block DC coefficients obtaine by DC on each image block. he principal components of atermark are embee into the constructe matrix. he atermarke image is obtaine after a series of inverse transformations. he remaining part of the paper is organize as follos. Section 2 gives the preliminaries, hich inclues the SVD transform, the basic SVD-base atermarking scheme, the eakness of the SVD-base atermark scheme, the SSVD an evaluation criterion of atermark scheme. he propose atermark metho is esigne in Section 3, incluing the proceure of atermark embeing an atermark extraction. Experimental results on the propose scheme are shon in Section 4. Conclusions an remarks on possible further ork are given finally in Section Preliminaries In this section, e revie the SVD transform, the basic atermarking scheme base SVD [4] an point out the eakness of the metho. Besies that, the SSVD is introuce briefly in this section. he evaluation criterion is given here, hich inclues normalize correlation (NC) an peak-signal-to-noise ratio (PSNR) Singular Value Decomposition SVD is an effective numerical analysis tool use in matrices analysis. In the SVD transform, a matrix N N can be resolve into three parts A 1 ith size of A1 U1S1V 1 u1, u2,, un v1, v2,, vn 0 0, (1) here the U1 an V1 are the N1 N1 unitary matrices. [ u1, u2,, u N ] an [ v1, v2,, v N ] are the column vectors. S1 is an n n iagonal matrix, here iag( 1, 2,, r ). he iagonal element i enotes the ith singular value of A 1. he SVD transformation can be performe on an image of any size. It shoul be note that the to unitary matrices of an image, U1 an, specify the geometric information of an image, hile the singular values image [11]. S 1 V 1 represent the luminance information of an 192 Copyright c 2016 SERSC

3 2.2. SVD-base Watermarking Metho In the section, e revie briefly the SVD-base atermarking metho in [4]. he process of atermark embeing is as follos. Firstly, the SVD is applie on the host image, A 2 A U S V. (2) hen, a atermark matrix W 1 is ae into the singular value matrix S 2, an the ne matrix is ecompose into,, an by SVD transform, here is the scaling factor that etermines the embeing intensity of W 1, S W 2 1 S W U S V. (3) he atermarke image A 1 is obtaine by A U S V. (4) In the proceure of atermark extraction, the atermark W 1 is extracte from the atermarke image. he atermark extraction can be escribe as follos, A 1 A U S V, (5) D U S V, (6) W ( D S ) /. (7) Note that the matrices,, an are the keys in the atermark extraction. he above is the hole SVD-base atermarking scheme. U 1 S 2 V 1 U Weakness of SVD-base Watermarking Metho An attack as propose in [9], aiming at the SVD-base atermarking scheme [4] mentione in Section 2.2, hich as also calle as the false positive problem. After performing SVD on an image, the matrices an represent the etails an structure of the image, hile the matrix specifies the luminance of the image state previously in Section 2.1. In [9], the authors have pointe out that in Eq. (7), the extracte atermark an D 1 ere ifferent just in the iagonal values, hich as up to the scaling factor. Accoringly, the malicious image attackers can use the fake V W 1 S 2 U 2 S 1 V 2 V 1 U f, f f f 1 f matrices, hich are from S2 Wf Uf SfV f, to create a fake matrix as D U S V in Eq. (6). As a result, the extracte atermark in Eq. (7) ill be similar to the fake atermark rather than the original atermark. It is obvious to be seen that a line across the iagonals of the fake extracte atermark. Suppose the original cover image is available in [9], the attackers coul use the same steps to embe the fake atermark in the original image an obtain the fake singular matrices U f an V f. hen U f an V f are use to make a false positive atermark in the process of atermark etection. Figure 1 shos the results of false positive test, in hich the fake extracte atermarks are extracte from the atermarke image Lena by using ifferent fake atermarks (Cameraman an Baboon, etc.). By moifying the singular value of the original atermark, one can easily create their on atermark as shon in Figure 1 (e) ~ (h). From Figure 1, e can see that a serious false positive problem exists in the atermarking scheme [4] base on SVD. Furthermore, the problem exists in [5-8]. By comparing these schemes, it is not ifficult to fin such a fact that the key step of Copyright c 2016 SERSC 193

4 embeing atermark is S W U S V, an the matrices, U 1 an V 1, are eeme as a part of keys in the proceure of atermark extraction. an represent the etails an construction of the atermark state previously. So it shoul be avoie embeing the atermark into the cover image by Equation (3). U 1 V 1 (a) (b) (c) () (e) (f) (g) (h) Figure 1. False Positive est: (a) Original Image Lena; (b) Original Watermark Image Boat; (c) Watermarke Image; () Extracte Watermark Using Its On Keys; (e) Extracte Fake Watermark Cameraman; (f) Extracte Fake Watermark Baboon; (g) Extracte Fake Watermark House; an (h) Extracte Fake Watermark Peppers 2.4. Shuffle Singular Value Decomposition SSVD as propose in [13], hich is a variation on SVD-base image compression, actually. he change can be regare as a pretreatment step in hich the input image is scramble, after hich it is performe to the stanar SVD transformation. he moification comprise a pretreatment step, hich is expresse as X S ˆ( A 3 ), here enotes the shuffle operator an is the permute image. he size of the image 2 is N2 N2 an N2 n1, here n 1 is integer. X S ˆ( A 3 ) is forme by to steps. In the first place is ivie into n1 n1 blocks. An then taking the block in ro major orer an placing its image pixel values in ro priority to constitute the ro of. Figure 2 emonstrates that the reconstructe image generate by SSVD is better than the image reconstructe by SVD in visual quality uner the same image rank conition. In Figure 2, there are a couple of concepts to explain. he SVD transform is applie on image matrix A 3, namely, A 3 U S V. A rank-r approximation to A 3 is the matrix Ar Ur SrV r, here S r is the top-left r r sub-matrix of S ; U r is mae up of the first columns of U ; an V r consists of the first ros of V. Xr U r Sr V r enotes the approximation image, hich is similar to X. he PSNR value comparison beteen SSVD an SVD is shon in Figure 3. It can be seen that PSNR value increases as the groth of rank r, hich signifies that the approximation image is more an more similar to the original image Barbara. In aition, it is obvious that the PSNR values of SSVD are better than that of SVD uner the same X r A 3 X r ith ith Ŝ A Copyright c 2016 SERSC

5 r ˆ 1 S ( X r ) rank, hich means that the image is a much better approximation for. From Figure 2 an Figure 3, e can observe intuitively that the performance of reconstructe image by SSVD is better than that of reconstructe image by SVD. he avantages of SSVD in reconstructe image are the vision effect of approximation an PSNR values in Figure 2 an Figure 3, respectively. A 3 (a) (b) (c) () (e) (f) (g) (h) (i) (j) (k) Figure 2. SVD an SSVD Applie on Image Barbara ith Size of : (a) Original Image Barbara; (b) Approximation Image ith SVD Rank-2; (c) Approximation Image ith SSVD Rank-2; () Approximation Image ith SVD Rank-5; (e) Approximation Image ith SSVD Rank-5; (f) Approximation Image ith SVD Rank-10; (g) Approximation Image ith SSVD Rank-10; (h) Approximation Image ith SVD Rank-20; (i) Approximation Image ith SSVD Rank-20; (j) Approximation Image ith SVD Rank-50; an (k) Approximation Image ith SSVD Rank-50 Copyright c 2016 SERSC 195

6 60 55 SVD SSVD PSNR Evaluation Criterion rank Figure 3. PSNR Comparison beteen SVD an SSVD he PSNR value beteen the original image atermarke image is efine as 2 max PSNR 10 log 10 (B) EMSE I I ith size of M N an the ( I ), (8) E 1 MN i j i j N1 M1 2 MSE ( (, ) (, )) i0 I I, (9) j0 here I max is the maximum possible pixel value of original image I, an for to images having 8 bits per pixel, is often chosen as 255, so it is in this paper., hich is the abbreviation of mean square error beteen the original image an the atermarke image, is efine by Eq. (9). ypical PSNR value ranges from 30 B to 50 B. he higher PSNR value is, the harer to istinguish the ifference beteen the original image an atermarke image by human nake eyes [14]. he NC beteen the original atermark an extracte atermark is obtaine as NC( WW, ) W I i I max W( i, j) W ( i, j) j W( i, j) W ( i, j) 2 2 i j i j I E MSE, (10) here represents the original atermark an represents the extracte atermark. he NC is a positive value hich is less than or equal to 1. A larger NC value means that the extracte atermark is more similar to the original atermark. 3. Propose Watermarking Scheme In the section, a novel atermarking metho base on DW-DC an SSVD is propose, hich contains atermark embeing an atermark extraction. he etails of the to processes are escribe in later sub-sections. W 196 Copyright c 2016 SERSC

7 3.1. Watermark Embeing In the proceure of atermark embeing, there are several basic points concerning DW an DC. We ill introuce them briefly. DW is performe on an image, hich is ivie into four sub-bans, a lo resolution approximation sub-ban (LL), horizontal (HL), vertical (LH), an iagonal (HH) etail sub-bans. In [3], Huang et. al., presente that the better robustness can be realize if atermark is ae in irect current (DC) components because DC components have much greater perceptual capacity than alternating current (AC) components. Accoringly, the DC components are chosen as the location of embeing atermark. Figure 4 shos a block iagram of atermark embeing. he etaile steps of embeing the atermark W ith size of into a M M gray scale host image are shon belo: Step 1: he SSVD is performe on W I by N N 2 2 W U S V. (11) Step 2: he principal components are obtaine by Eq. (12). he rest components atermark extraction, W, hich ill be embee into the cover image, V are viee as a part of keys in W U S. (12) Step 3: he host image is ecompose by one-level avelet transformation ith Haar avelet, an the sub-ban ith size of M / 2 M / 2 is obtaine. Step 4: he is ivie into m m non-overlapping image blocks an the block size is chosen as m M /2N2. Let B ( i, j) enotes the position of an image block at, here i, j 1,2,, N2. Step 5: Apply DC on each image block B ( i, j). A ne matrix is compose of ( i, j) LL 1 LL 1 DC components extracte from each block DC coefficients, an nees to be store, hich ill be use to extract atermark information in atermark extraction. Step 6: he principal components of W are embee into, B ( i, j) B( i, j) W ( i, j), (13) here B ( i, j) is the istorte DC component in image block ( i, j ) an enotes the embeing strength. Step 7: Inverse DC is performe on each image block an the DC components ( i, j) are replace by the corresponing values in. After this step, the istorte LL is obtaine. Step 8: he atermarke image is reconstructe by applying one-level inverse DW. I B B B B Copyright c 2016 SERSC 197

8 Apply DC DC Extract DC component One-level DW B Original image I Perform SSVD Original atermark W Watermarke image I 3.2. Watermark Extraction Inverse DW Obtain the principal components W U S V W U S B ( i, j) B ( i, j) W ( i, j) LL Figure 4. Block Diagram of Watermark Embeing Moify the coefficients Inverse DC In this subsection, the atermark W is extracte from the atermarke image possibly corrupte by ifferent attacks. Figure 5 shos the schematic iagram of the atermark extraction. Its etaile steps are as follos. Step 1: One-level Haar DW is applie on the atermarke image, an the lo frequency sub-ban Step 2: * 1 * LL 1 is acquire. LL is ivie into non-overlapping image blocks ith size of I I mm, hich are enote as B ( i, j), here i, j 1,2,, N2. Step 3: DC is performe on each block B ( i, j) an the DC component is extracte from each image block to organize the ne matrix. Step 4: he principal components are obtaine by W [ B ( i, j) B ( i, j)] /. (14) W Step 5: he extracte atermark is obtaine as W W V. (15) B 198 Copyright c 2016 SERSC

9 Apply DC DC Extract DC component One-level DW B Watermarke image V, B, Keys I W ( i, j) [ B ( i, j) B ( i, j)] Extract the principal components - + Inverse SSVD W W V W Figure 5. Block Diagram of Watermark Extraction Extracte atermark 4. Experimental Results an Performance Analysis In the section, the experiments ith regar to the imperceptibility an robustness of the esigne scheme an the false positive test are given. Otherise, compare ith other reliable atermark methos, the propose scheme shos its avantages an isavantages. All results are presente in the form of tables an figures, hich can convey the effect intuitively. Besies that, the propose scheme is calle as propose metho 1, hile the scheme is calle as propose metho 2 ue to the fact that SSVD is replace by SVD Imperceptibility an Robustness est In the section, some experiments are one to evaluate the propose scheme s imperceptibility an robustness. he original cover image an the atermark image are gray scale images ith size of an 64 64, respectively. In the proceure of atermark embeing, the size of the image block is 4 4. he imperceptibility beteen original image an atermarke image is measure by PSNR, hile the quality of extracte atermark is evaluate by NC mentione previously. he PSNR value is use to measure the similarity beteen the original image an the atermarke image, hile the NC value evaluates the similarity beteen extracte atermark an original atermark. In the experiment to test the imperceptivity, the atermark image is embee into ifferent host images in ifferent scaling factors. he PSNR an NC values are recore in able 1 an able 2. From the to tables, it is obvious that it is appropriate that the embeing intensity is set as 0.10 to balance the imperceptibility an robustness. Copyright c 2016 SERSC 199

10 able 1. PSNR Values of Watermarke Images uner Different Embeing Intensities Embeing intensities Lena Baboon Boat Brige able 2. NC Values of Extracte Watermarks uner Different Embeing Intensities Embeing intensities Lena Baboon Boat Brige Figure 6 shos the results of atermark embeing an atermark extraction ithout any attacks, here is chosen as In orer to test the robustness of the propose metho, various attacks are ae to the atermarke image an extracte atermark is taken from the atermarke images uner attacks. Figure 7 shos the atermarke images uner attacks an Figure 8 shos the extracte atermark corresponing to the attacke atermarke images in Figure 7. From able 1, ith the increasing embeing strength, PSNR value is on the ecline, hich means that the imperceptibility of atermark is getting orse. From Figure 7 an Figure 8, e can see that the extracte atermarks are ientifiable after the atermarke image corrupte by a number of attacks, such as Salt an Pepper noise, Gaussian noise, JPEG compression, ifferent filtering methos like mean filter an meian filter, image sharpening an image cropping, etc. (a) (b) (c) () Figure 6. Imperceptibility of the Watermark Scheme: (a) Host Image; (b) Original Watermark; (c) Watermarke Image; an () Extracte Watermark 200 Copyright c 2016 SERSC

11 (a) (b) (c) () (e) (f) (g) (h) (i) (j) (k) (l) Figure 7. Watermarke Images uner Various Attacks: (a) Gaussian Noise (0, 0.001); (b) Salt an Pepper Noise (0.01); (c) JPEG Compression (QF = 70); () JPEG Compression (QF = 50); (e) JPEG Compression (QF = 20); (f) Image Rescaling ( ); (g) Meian Filter (3 3); (h) Mean Filter (5 5); (i) Mean Filter (3 3); (j) Image Sharpening; (k) Image Cropping; an (l) Speckle Noise (0.01) (a) (b) (c) () (e) (f) (g) (h) (i) (j) (k) (l) Figure 8. Extracte Watermarks uner Corresponing Attacks in Figure 7 In able 3, the embeing information is ifferent. W is efine as W S V an is set as hen the PSNR values are B an B corresponing to W an W, respectively. he NC values are inclue in able 3, hich are use to SV estimate the similarity beteen the extracte atermarks an original atermarks (ifferent principal components). Base on the results on able 3, the propose scheme has strong ability to resist various attacks ae into the atermarke images. Besies, the PSNR values of the atermark scheme are still ithin the acceptable limit, hich SV SV Copyright c 2016 SERSC 201

12 inicates that the atermark embeing in the image cannot be foun by human vision system. able 3. est of Imperceptibility an Robustness of the Designe Scheme NC 4.2. False Positive est Embeing information W W SV Embeing strength PSNR (B) Gaussian noise (0, 0.001) Salt an Pepper noise (0.01) JPEG compression (QF = 70) JPEG compression (QF = 50) JPEG compression (QF = 20) Image rescaling Meian filter (3 3) Meian filter (5 5) Mean filter (3 3) Image sharpening Image cropping Speckle noise (0.01) In this experiment, an experiment concerning the false positive problem is conucte for the esigne atermark scheme. is set as 0.10, hile its PSNR value is B an its NC value is We conucte a false positive test by replacing the key from the original atermark image ith other images such as Cameraman an Peppers. Figure 9 (a) an (b) sho the host image an the atermark, respectively, hile (c) is the extracte atermark. Figure 9 ()~(h) are obtaine by changing the key taken from Cameraman an Peppers, etc. Besies that, the other steps are same exactly. From the results in Figure 9, they inicate that the propose scheme can avoi the false positive problem mentione in [8] Comparison ith Other Reliable Watermark Schemes In this section, this experiment is conucte to compare the performance of the propose metho ith other reliable atermark methos [11, 12]. he scheme mentione in [11] embee the principal components W of the atermark into the singular value matrix of the original image by using an appropriate embeing strength. In the proceure of atermark extraction, the original host image an the inverse matrices 1 1 ( U an ( V ) ) are necessary to extract the atermark from the atermarke image possibly corrupte by various attacks, hich also increase storage requirement an computational buren. In [12], the original image I is not neee in the atermarking scheme an avois the operation of matrix inversion. he ifference beteen the propose scheme an [12] is that the ne constructe matrix to embe the principal components is compose of DC component of each image block, hile the ne constructe matrix inserte the principal components is organize by the largest singular value of each image block. he comparison results are shon in able 4, in hich the PSNR an NC values are liste. It is obvious that the propose metho an the other to methos have their on avantages an isavantages. he imperceptibility of the propose scheme is better than the other to schemes uner the same embeing strength. he propose atermark scheme is robust to resist the attacks mentione in able 4. he I V V 202 Copyright c 2016 SERSC

13 propose metho 1 is that the atermark is applie by SSVD, hile SVD is performe on the atermark in the propose metho 2. (a) (b) (c) () (e) (f) (g) (h) Figure 9. Results of the False Positive Problem: (a) Original Image Lena; (b) Original Watermark; (c) Extracte Watermark Using Its On Keys; () Extracte Watermark Using Cameraman; (e) Extracte Watermark Using Baboon; (f) Extracte Watermark Using Boat; (g) Extracte Watermark Using Brige; an (h) Extracte Watermark Using Peppers NC able 4. Comparison ith Other Reliable Watermark Schemes Methos Guo an Jain et. Propose Propose Prasetyo al., [11] metho 1 metho 2 [12] Embeing strength PSNR (B) Gaussian noise (0, 0.001) Salt an Pepper noise (0.01) JPEG compression (QF = 70) JPEG compression (QF = 50) JPEG compression (QF = 20) Image rescaling Meian filter (3 3) Meian filter (5 5) Mean filter (3 3) Image sharpening Image cropping Speckle noise (0.01) Conclusions A novel atermark scheme base DW-DC an SSVD is propose in this paper. A novelty of the propose scheme is that the principal components are chosen to embe into Copyright c 2016 SERSC 203

14 the constructe matrix compose of DC component of DC coefficients in each image block, instea of the hole atermark, hile the rest components of the atermark are viee as a part of keys neee in the process of atermark extraction. he atermark scheme avois the false positive problem occurre in other atermark schemes base on SVD. Otherise, it shos excellent robustness in resisting attacks such Gaussian noise, Salt an Pepper noise, image cropping, an meian filter, etc. he propose scheme can be extene to the vieo, auio, color image or other orks. Hoever, there are a fe isavantages in this propose scheme, hich is the poor performance in resisting some attacks like rotation, affine transformation, translation, an image-enhancement, etc. One of the orientations of the improve atermark scheme is to be robust to the attacks state above. Acknolegments his ork as supporte by the National Natural Science Founation of China (No ), the Natural Science Founation of Shanong Province, China (No. ZR2015PF004), an the promotive research fun for excellent young an mile-age scientists of Shanong Province, China (No. BS2013DX022). he authors thank Heng Zhang an Yunpeng Zhang for their kin help an valuable suggestions in revising this paper. he authors also thank the anonymous revieers an the eitors for their valuable comments to improve the presentation of the paper. References [1]. Hai, C. M. Li, J. M. Zain an A. N. Aballa, Robust image atermarking theories an techniques: A revie, Journal of Applie Research an echnology, vol. 12, no. 1, (2014) Feb., pp [2] Z. Y. An an H. Y. Liu, Research on igital atermark technology base on LSB algorithm, Proceeings of the 4th International Conference on Computational an Information Sciences, Chongqing, China, (2012) Aug , pp [3] J. W. Huang, Y. Q. Shi an Y. Shi, Embeing image atermarks in DC components, IEEE ransactions on Circuits an Systems for Vieo echnology, vol. 10, no. 6, (2000) Sept., pp [4] R. Z. Liu an. N. an, An SVD-base atermarking scheme for protecting rightful onership, IEEE ransactions on Multimeia, vol. 4, no. 1, (2002) Mar., pp [5] J. Varghese, S. Subash, O. B. Hussain, M. R. Saai, B. Babu, M. S. Khan an J. M. Basheer, An efficient DC-SVD base algorithm for igital image atermarking, Proceeings of the International Carnahan Conference on Security echnology, Rome, Italy, (2014) Oct , Article number: , 6 pages. [6] R. Islam an J. M. Kim, Reliable RGB color image atermarking using DW an SVD, Proceeings of the 3r International Conference on Informatics, Electronics an Vision, Dhaka, Banglaesh, (2014) May 23-24, Article number: , 4 pages. [7] X. Y. Ye, X.. Chen, M. Deng, S. Y. Hui an Y. L. Wang, A multiple-level DC base robust DW-SVD atermark metho, Proceeings of the 10th International Conference on Computational Intelligence an Security, Kunming, China, (2014) Nov , pp [8] S. Fazli an M. Moeini, A robust image atermarking metho base on DW, DC, an SVD using a ne technique for correction of main geometric attacks, Optik - International Journal for Light an Electron Optics, vol. 127, no. 2, (2016) Jan., pp [9] X. P. Zhang an K. Li, Comments on An SVD-base atermarking scheme for protecting rightful onership, IEEE ransactions on Multimeia, vol. 7, no. 2, (2005) Apr., pp [10] Y. Naerahmaian an S. Beheshyi, A realistic attack on SVD base atermarking scheme, Proceeings of the 28th IEEE Canaian Conference on Electrical an Computer Engineering, Halifax, Canaa, (2015) May 3-6, pp [11] C. Jain, S. Arora, an P. K. Panigrahi, A reliable SVD base atermarking scheme, Computer Science, (2008), 8 pages. [12] J. M. Guo an H. Prasetyo, False-positive-free SVD-base image atermarking, Journal of Visual Communication an Image Representation, vol. 25, no. 5, (2014) Jul., pp [13] A. Ranae, S. S. Mahabalarao, an S. Kale, A variation on SVD base image compression, Image an Vision Computing, vol. 25, no. 6, (2007) Jun., pp [14] O. O. Khalifa, Y. B. Yusof, an R. F. Olanreaju, Performance evaluations of igital atermarking system, Proceeings of the 8th International Conference on Information Science an Digital Content echnology, Jeju Islan, South Korea, (2012) Jun , pp Copyright c 2016 SERSC

15 Authors Zhi Zhang, he as born in Shanong province, China, in He receive his B.E. egree in electronic information engineering from Shanong University of Science an echnology, China, in He is currently pursuing his M.E. egree in information an communication engineering at Shanong University, China. His current research interests inclue igital image processing an computer vision. Chengyou Wang, he as born in Shanong province, China, in He receive his B.E. egree in electronic information science an technology from Yantai University, China, in 2004, an his M.E. an Ph.D. egrees in signal an information processing from ianjin University, China, in 2007 an 2010, respectively. He is currently an associate professor an supervisor of postgrauate stuents ith the School of Mechanical, Electrical an Information Engineering, Shanong University, Weihai, China. His current research interests inclue igital image/vieo processing an analysis (transform coing, eblocking, emosaicking, igital atermarking, image ehazing, image quality assessment, etc.), computer vision (image retrieval, tamper etection, etc.), pattern recognition an machine learning, an multiimensional signal an information processing. Xiao Zhou, she as born in Shanong province, China, in She receive her B.E. egree in automation from Nanjing University of Posts an elecommunications, China, in 2003; her M.E. egree in information an communication engineering from Inha University, Korea, in 2005; an her Ph.D. egree in information an communication engineering from singhua University, China, in She is currently a lecturer an supervisor of postgrauate stuents ith the School of Mechanical, Electrical an Information Engineering, Shanong University, Weihai, China. Her current research interests inclue ireless communication technology, an igital image processing an computer vision. Copyright c 2016 SERSC 205

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