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The Journal of Systems and Software 83 (2010) 1015–1028

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The Journal of Systems and Software

journal homepage: www.elsevier .com/locate / jss

Robust lossless image watermarking based on a-trimmed mean algorithmand support vector machine

H.-H. Tsai a,*, H.-C. Tseng a, Y.-S. Lai b

a Department of Information Management, National Formosa University, No. 64, Wunhua Rd., Huwei Township, Yunlin County 632, Taiwanb Department of Computer Science and Information Engineering, National Chung Cheng University, 621, Taiwan

a r t i c l e i n f o

Article history:Received 2 July 2009Received in revised form 19 October 2009Accepted 25 December 2009Available online 4 January 2010

Keywords:Copyright protectionDiscrete wavelet transformSupport vector machineWatermark extractionLossless watermarking

0164-1212/$ - see front matter Crown Copyright � 2doi:10.1016/j.jss.2009.12.026

* Corresponding author.E-mail address: [email protected] (H.-H. Tsai).

a b s t r a c t

This paper presents a robust lossless watermarking technique, based on a-trimmed mean algorithm andsupport vector machine (SVM), for image authentication. SVM is trained to memorize relationshipbetween the watermark and the image-dependent watermark other than embedding watermark intothe host image. While needing to authenticate the ownership of the image, the trained SVM is used torecover the watermark and then the recovered watermark is compared with the original watermark todetermine the ownership. Meanwhile, the robustness can be enhanced using a-trimmed mean operatoragainst attacks. Experimental results demonstrate that the technique not only possesses the robustnessto resist on image-manipulation attacks under consideration but also, in average, is superior to otherexisting methods being considered in the paper.

Crown Copyright � 2009 Published by Elsevier Inc. All rights reserved.

1. Introduction

Nowadays, digital watermarking schemes are widely used inthe protection of the ownership of digital images to avoid the ille-gal behavior pirating intellectual property rights. Many methodsdeveloped in spatial domain or frequency domain, modify the im-age contents during watermark embedding (Chang and Su, 2005).The methods cause image distortion. Therefore, they cannot be di-rectly applied to protect the medical images or digital archiveproducts due to that the modifications to these images are notallowable. In recent years, lossless watermarking techniques havebeen proposed to enhance the image visual quality and have beenapplied to protect the intellectual property rights of the imagesmentioned above.

The lossless watermarking can be categorized into two kinds ofwatermarking methods. One is called the reversible watermarkingwhich is often used for image authentication, in which the embed-ded information can be extracted from the host image and the ori-ginal host image can be recovered completely (in this sense, thehost image is lossless from the watermark embedding) (Fridrichet al., 2002). In Vleeschouwer et al. (2003), the Bijective techniqueis employed in the design of their watermark embedding proce-dure. However, the technique is fragile while suffering from at-tacks. Mobasseri and Berger (2005) adopted compressionalgorithms to embed watermarks. The computational complexity

009 Published by Elsevier Inc. All r

of the method will rapidly increase for the case of large water-marks. Celik et al. (2006) used the generalized-least significant-bit (G-LSB) scheme to compress the watermark. Subsequently, ver-ification information and the compressed watermark are embed-ded in the LSB of the target pixels in spatial domain. The limit ofthe scheme is insufficient payloads for the case of largewatermarks.

The other is called the zero-watermark which is used for copy-right protection, in which the watermark is not embedded into thehost image (in this sense, the host image is lossless) (Chen and Zhu,2007, 2008). Shieh et al. (2005) employed visual cryptography todevise a zero-watermarking technique in the wavelet domain.Chen et al. (2005) proposed a zero-watermarking scheme devel-oped in the wavelet domain. The scheme generates a digital signa-ture of an image by comparing the coefficients in a low-frequencysubband with the average of them. Subsequently, the scheme ap-plies the XOR operation to the signature and owner signature toproduce the associated verification key. A public key cryptosystemis involved in the scheme so as to ensure that the verification keycan be securely arrived at the receiver site upon the watermarkrecovery for image-authentication verification. Sang and Alam(2005) exploited a trained neural network (TNN) to memorizethe relationship between the gray-value of a pixel and that of itsneighbors. A digital signature of an image is then obtained by com-paring the pixel values with their physical outputs of the TNN. Theoutputs can be gained by feeding the TNN with an input vectorcomprising the values of the pixel as well as its neighbors. The pro-cess of calculating a verification key in the method is like Chen’s

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1016 H.-H. Tsai et al. / The Journal of Systems and Software 83 (2010) 1015–1028

method. Subsequently, the key is transmitted to the receiver siteusing a public key cryptosystem. Additionally, Chang and Lin(2008) employ the edge information in spatial domain to constructa verification key for an image. The rest of Chang’s method is alsolike Chen’s method.

Each zero-watermarking method mentioned above needs anassumption that the verification key should be correctly receivedat the receiver site during watermark extraction. However, the ver-ification key may be corrupted through an unreliable channel. Con-

a

b

c

Fig. 1. (a) X is segmented into bL8c � bK8c non-overlapped blocks with size 8 � 8. (b) bij is t

with size 8 � 8 at level 2.

sequently, the above methods have weak robustness if the key hasone error bit. In order to solve the problem, this inspires us to pro-pose an effective zero-watermarking technique based on a-trimmed and SVM. It does not damage the original image duringwatermark embedding because it first uses a trained SVM to mem-orize the watermark and then exploits the trained SVM to estimatethe watermark (Chang and Lin, 2007). Moreover, the techniqueutilities a-trimmed mean operator against noise attacks so that itcan be enhanced in robustness. Experimental results demonstrate

ransformed through DWT. (c) Components are constituted of a wavelet image block

H.-H. Tsai et al. / The Journal of Systems and Software 83 (2010) 1015–1028 1017

that the technique not only possesses the robust ability to resist onimage-manipulation attacks under consideration but also, in aver-age, is superior to other existing methods being considered in thepaper.

The rest of the paper is organized as follows. Section 2 reviewsthe discrete wavelet transformation (DWT) and the SVM. Section 3then describes the feature selection, signature-embedding, and sig-nature-extraction algorithms used in the design of the technique.

-1 1 1 1 1 1 1 1 -1 1 -1 -1 1 1 -1

1 1 -1 -1 1 1 -1 1 -1 1 1 -1 1 1 1

sliding window

mapping

owner’s signature

1

1 -1 1 -1 1

image-dependent watermark

Fig. 2. Tsai’s method uses a sliding window to generate the training pattern forSVM.

-1 1 1 1 1 1 1 1 -1 1 -1 -1 1 1 -1

1 1 -1 -1 1 1 -1 1 1 1 1 -1 1 1 1

mapping

owner’s signature W

1

image-dependent watermarkW f1

Error bit

-1

the nth test pattern

the (n+1)th test pattern



Error

Error

Error

Error

1 -1

Fig. 3. Contiguous test patterns include the same error bit.

ξ

original image

X

DWT

PRNG

-trimmed mean

BS

kρ

kBρ

kbρ

ftw

seeds: ,( 212 ss=s

FS

PRNG

Fig. 4. The block diagram of th

Section 4 shows experimental results. Finally, Section 5 drawsconclusions.

2. A review of DWT and SVM

Let X = [xq]L�K denote a gray-level image with size L � K wherexq e {0, 1, . . . , 255} represents a gray level of the pixel at positionq = (i, j) e {1, . . . , L} � {1, . . . , K}. Here a stamp binary image withsize m is taken as the watermark W. Using the row-major method,the watermark W and its extracted version can be, respectively, de-noted by

W ¼ ðw1;w2; . . . ;wk; . . . ;wmÞ ð1Þ

and

cW ¼ ðw1; w2; . . . ; wk; . . . ; wmÞ; ð2Þ

where wk and wk 2 f�1;1g.

2.1. DWT

Fig. 1a depicts that X be segmented into non-overlapped blocksbL

8c � bK8c with size 8 � 8. Fig. 1b illustrates that each non-over-lapped block bij in X is decomposed through the DWT. Let Bij repre-sent the corresponding wavelet block consisting of 64 coefficients.Fig. 1c shows bij is transformed through DWT with two levels. Eachwavelet block Bij at the lth level consists of four bands (compo-nents): low–low band (LLl), low–high band (LHl), high–low band(HLl), and high–high band (HHl). LL1 band can be further dividedinto four subbands: low–low subband (LL2), low–high subband(LH2), high–low subband (HL2), and high–high subband (HH2).Generally, HL2, LH2, HH2, HL2, and LH2 are called middle-frequencycomponents (or detailed subbands). Moreover, in Fig. 1c, Bq,l,k(r, c)stands for the coefficient at the position (r, c) on the block Bq,l,k

where l denotes bq is transformed through DWT with the lth leveland k belongs to {1, 2, 3, 4} = {LL, HL, LH, HH} where k = 1, 2, 3, and4 represents LL, HL, LH, and HH, respectively. Thus, Bqk ;2;2ð0;0Þ is

Φ

owner's signature W

seeds: ),( 12111 ss=s

kw

SVM

TPC

)22

e watermark embedding.

1 -1 1 1 1 -1 1 1 1

1 1 1 -1 1 1 -1 1 1 -1

-1 1 1 -1 1 1 -1 1 1 -1

1 1 -1 1 -1 1 1 -1

1 1 -1 1 -1 1 1 -1

1 1 -1 1 -1 1 1 -1

1 1 -1 1 -1 1 1 -1

1 1 -1 1 -1 1 1 -1

1 1 -1 1 -1 1 1 -1

1 1 -1 1 -1 1 1 -1

1 1 -1 1 -1 1 1 -1

kw

image-dependent watermark Ξ

owner signature

training pattern n

W

training pattern n+1

Traget features

1 -1

Fig. 5. A confusion example that two training patterns have the same input vector but different desired output.

1 -1 1 1 1 -1 1 1 1

kT

v

v

kw

image-dependent watermark Ξ

owner signaturetraining pattern

W

1

Fig. 6. The construction of a training pattern by using W and N.


the coefficient at the position (0, 0) on the subband HL2 of the blockBqk

.

2.2. SVM

Let F = {(xi, di)|i = 1, 2, . . . , N} be a set of N training patterns,where xi = (xi1, xi2, . . . , xin) e Rn denotes an input vector in the inputspace and di e {�1, 1} represents the label class of xi. Assume F be alinear separable. A linear SVM seeks an optimal separating hyper-plane with maximum margin so that the linear SVM has good gen-eralization. More specially, the SVM searches a pair (w, b) for anoptimal hyperplane by maximizing the margin 2

kwk betweenwxt

i þ b ¼ 1 and wxti þ b ¼ �1 where w e Rn, b e R, and t denotes

the transpose operation. Assume the pair (wo, bo) is an optimalsolution for a corresponding separating hyperplane for F, the linearSVM can be performed by the decision function f defined as

f ðxÞ ¼ sgnðwoxt þ boÞ ð3Þ

where sgn(�) stands for the sign function.While classifying a set of linearly non-separable data, slack vari-

ables ni are added into the derivation of a linear non-separable

SVM. Namely, linear SVM has the following optimization problem(Burges, 1998)

Minimize12

wtwþCXN

i¼1

ni

Subject to diðwxti þbÞP 1� ni; i¼ 1; . . . ;N; and ni P 0; i¼ 1; . . . ;N:

ð4ÞAdditionally, C denotes the penalty for vectors incorrectly classifiedor inside the margin. A linear SVM, however, is inadequate for non-linear data. Thus, kernel functions, K(�, �), are introduced in the lin-ear SVM to construct the nonlinear SVM (Burges, 1998; Tsai andSun, 2007). Let the pair ( �w; �b) be computed by using an optimiza-tion solution �a ¼ ð�a1; �a2; . . . ; �aN). Then, the decision function f for anonlinear SVM can be rewritten as

f ðxÞ ¼ sgnð �wtxþ �bÞ ¼ sgnXN

i¼1

�aidiKðxi; xÞ þ �b

!: ð5Þ

3. The proposed lossless watermarking scheme

The method, proposed in Tsai et al. (2007), takes the LL4subband of image blocks to construct the feature information.

ξ ′

extracted owner’s signature

protected image

X

DWT

W

seeds: 11 121 ),( ss=sPRNG

-trimmed mean

TSVM

BS

kρ

'k

B ρ

'k

bρ

TPC

ftw

Φ ′

seeds: 12 222 ),( ss=s

FS

PRNG

Fig. 7. The structure of the watermark extraction.


The procedure is to use the difference between the sum of the coef-ficients in the upper half of each selected block and the sum of thecoefficients in the lower half of the block. The method then appliesthe XOR operation to generate the verification. A drawback of themethod is that it fails when suffering from some attacks in spatialdomain such as cropping and painting attacks. The method utilizesa sliding window to produce the training and test patterns for SVM.Fig. 2 shows an illustration for the process of generating patterns.Another drawback of the method is to get more incorrect test pat-terns if image-dependent watermark includes error bits. An exam-ple is exhibited in Fig. 3. An error bit is collected in severalsequential training patterns. Accordingly, the performance of the

Fig. 8. (a) The original watermark W; (b) a scramble version of (a); (c) Lena; (d)Baboon; (e) Barbara; (f) Peppers; (g) Couple; and (h) Streambridge.

method is reduced since these incorrect patterns are fed to thetrained SVM to estimate owner’s signature. Larger size of a slidingwindow will create more error bits in the test patterns. As a result,the method has more poor performance. Therefore, the proposedtechnique adopts a-trimmed mean operator to enhance therobustness of the method in Pitas and Venetsanopoulos (1990)and Tsai and Sun (2007). Additionally, it does not require the ver-ification information so as to reduce the computational time fordata encryption and decryption.

3.1. Watermark embedding

Fig. 4 shows the block diagram of the watermark embedding.First, a pair of seeds, S1 = (s11, s12), is fed to the PRNG componentto generate a sequence C of random positions. PRNG denotes thepseudo-random number generator. It is realized by using a qua-dratic residue generator, the so-called Blum–Blum–Shub generator(BBSG) (Blum et al., 1986; Tsai and Sun, 2007). Let the length C bem. After feeding the block selection (BS) component with the origi-nal image and C, the BS component outputs a set, W, of m targetblocks to be embedded. Here the size of each target block, bqk

, isset to 64 � 64. Next, Bqk

represents the transformed block usingthe DWT for each block bqk

. The reason to adopt the block-wise ap-proach in the design of the proposed method is that blocks, whichare randomly selected, probably include less corruptions than awhole image.

Table 1Attacks used in the computer simulations of the paper.

Index Attacks Index Attacks

0 Attack-free 9 Blurring1 JPEG 10% 10 Brighten (+30)2 Median 3 � 3 11 Darken (�30)3 Median 5 � 5 12 Sharpen4 Median 7 � 7 13 Histogram equalization5 Noising 5% 14 Cropping (surround) 25%6 Gaussian noising 15 Painting7 SaltPepper noising 16 Average8 Scaling 1/4


Let Uqk ;l;1 stand for a sequence containing all coefficients in theLLl subband of Bqk

where l = 1, 2, 3, 4 and k = 1, 2, . . . , m. The detailscan be found in (6)

Uqk ;l;1 ¼ ðBqk ;l;1ð0;0Þ;Bqk ;l;1ð0;1Þ; . . . ;Bqk ;l;1ðrl; clÞÞ; ð6Þ

where rl = cl for each l. Moreover, r1, r2, r3, and r4 are 31, 15, 7, and 3,repectively. Subsequently, the feature selection (FS) componentcomputes the features of each block bqk

. The FS component appliesthe a-trimmed mean operation to each sequence Uqk ;l;1 to get Fk,l,in (7)

Fk;l ¼ a-trimmedðUqk ;l;1Þ; for l ¼ 1; . . . ;4; and k ¼ 1; . . . ;m: ð7Þ

The sequence n containing all Fk,l is specified as a form in (8). Finally,the feature information can be computed using (9) where h denotesa threshold

n ¼ fnt ¼ Fk;ljt ¼ lþ 4ðk� 1Þ; l ¼ 1; . . . ;4; and; k ¼ 1; . . . ;mg:ð8Þ

IF nt > ntþ1 and nt � ntþ1 > h then wft ¼ 1 else wf

t ¼ �1: ð9Þ

Fig. 9. Some attacked images which are manip

If the length of n is less than the size of the original image X, n is ex-panded to form a binary image N with size L � K. Here the binaryimage N is called the feature information or image-dependentwatermark for the original image X. In order to get better perfor-mance for training SVM, N is scrambled by using the PRNG compo-nent with another pair of seeds, S2 = (s21, s22). Fig. 5 depicts aconfusion example that two training patterns have the same inputvector but different desired output. Then, the training pattern col-lection (TPC) component constructs the training patterns by usingW and N. Fig. 6 depicts the construction of a training pattern byusing W and N. A set U of training patterns can be represented asa form in (10) where the input vector and the corresponding desiredoutput denote Tk and wk, respectively. Finally, the set U is used totrain an SVM

U¼fðTk;wkÞjk¼1;2;...;mg; where

Tk¼ðtkð0;0Þ;...;tkðv�1;v�1ÞÞ; tkði;jÞ2f1;�1g; ði;jÞ2f0;...;v�1g2:

ð10Þ

The embedding algorithm is summarized as follows.

ulated by the operations given in Table 1.


Step 1. Input X and W.Step 2. Feed the PRNG component with S1 to generate

C ¼ ð1; � � � ;qk; � � � ;qmÞ.Step 3. Collect W of m target blocks after feeding the BS compo-

nent with X and C.Step 4. For each bqk

where k = 1, 2, . . . , m.Step 5. Transform bqk

to be Bqkby using DWT with four levels.

Step 6. For Bqk, compute Uqk ;l;1 using (6) for all l.

Step 7. Apply the a-trimmed mean operator to calculate Uqk ;l;1

and then construct nt using (7) and (8) for all t.Step 8. Produce the feature information N using (9) and then

scramble N using the PRNG component with S2.Step 9. Feed the TPC component with W, N, and seeds to get the

training pattern set U in (9).Step 10. Train an SVM using U to get a trained SVM.

Cropping

0.94

0.95

0.96

0.97

0.98

0.99

1

47.5 45 42.5 40 37.5 35 32.5 30 27.5 25 22.5 20 17.5 15 12.5 10 7.5 5 2.5 0

BC

R

0.95

0.955

0.96

0.965

0.97

0.975

0.98

0.985

0.99

0.995

1

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

h

aver

age

BC

R

1

a

b

3.2. Watermark extraction

Fig. 7 illustrates the structure of the watermark extraction ofthe proposed technique. The PRNG, DWT, BS, FS, and TPC compo-nents are the same as those used in the watermark embeddingalgorithm of the proposed technique. Also, two pairs of seeds, S1

and S2, are identical to the seeds employed in the watermarkembedding algorithm of the proposed technique. Let X0 and N0 de-note the protected image (or damaged image) and its correspond-ing image-dependent watermark. U0 represents a set of inputvectors which are computed by feeding the TPC component withN0 and S2. Here the original watermark is needed for comparisonwhile estimating the original watermark (owner’s signature). Thatis, the orignal watermark is estimated by using a trained SVM(TSVM). The TSVM component performs watermark estimation.After feeding U0 into the TSVM component, the system outputsthe estimated watermark cW expressed as a form in (2).

The watermark estimation algorithm is summarized as follows.

Step 1. Input X0 and S1 = (s11, s12).Step 2. Feed the PRNG component with S1 to generate

C ¼ ð1; . . . ;qk; . . . ;qmÞ .Step 3. Collect W0 of m target blocks after feeding the BS compo-

nent with X0 and C.Step 4. For each b0qk

where k = 1, 2, . . . , m.Step 5. Transform b0qk

to be B0qkby using DWT with four levels.

Step 6. For Bqk, compute Uqk ;l;1 using (6) for all l.

Step 7. Apply the a-trimmed mean operator to calculate Uqk ;l;1

and then construct nt using (7) and (8) for all t.Step 8. Produce the feature information N0 using (9).Step 9. Feed the TPC component with N0 and S1 to get the set U0

of input vectors in (10).Step 10. Feed the TSVM component with the set U0 to obtain cW .

0.8

0.82

0.84

0.86

0.88

0.9

0.92

0.94

0.96

0.98

6 10 14 18 22 26 30 34 38 42 46 50 54 58block width v

aver

age

BC

R

c

Fig. 10. (a) Performance of using various a value in the a-trimmed mean algorithm;(b) performance of using various threshold h; (c) performance of using variousblock width v.

4. Experimental results

In this experiment, a 64 � 64 stamp binary image, as shown inFig. 8a, is regarded as the owner’s signature which is used to verifyimage copyrights. Fig. 8b displays a scramble version of Fig. 8a. Byusing a row-major algorithm, a binary image can be converted to abinary sequence with length 4096. Accordingly, m in (1) and (2) isset to 4096. Hereafter the binary sequence stands for the originalwatermark (owner’s signature) W. Bit Correction Rate (BCR), whichis defined by (11), is employed to measure the robust performanceof watermarking methods.

BCRðW;cW Þ ¼ 1�Pm

k¼1ðwk � wkÞ2m

: ð11Þ

Higher BCR conveys that an original watermark W more resemblesthe extracted watermark cW . Additionally, six test images are shownin Fig. 8c–h.

In order to investigate the robustness of the proposed method, at-tacks are simulated by using several image-manipulations. Table 1presents these attacks and their algorithms are briefly describedas follows. Fig. 9a–m shows some attacked images which aremanipulated by the operations given in Table 1.

� JPEG compressionWe applied JPEG on the test image induced by the StirMark-Benchmark 4.0 (Petitcolas et al., 2007).

� Median filteringThe median filter is a nonlinear digital filtering technique,often used to remove noise from images or other signals.The simulation tool can be found in StirMarkBenchmark4.0 (Petitcolas et al., 2007).

� NoisingWe applied the noising attack on the test image induced bythe StirMarkBenchmark 4.0 (Petitcolas et al., 2007).


� ScalingFirst, the size of the test image is reduced from 512 � 512pixels to 128 � 128 pixels. Subsequently, the size of testimage is enlarged from 128 � 128 pixels to 512 � 512 pixelsagain.

� BlurringThe operation applies the Gaussian blurring on the testimage with two pixels radius.

� BrightenThe operation adds 30 to each pixel of test image.

� DarkenThe operation subtracts 30 from each pixel of test image.

� SharpenWe applied the 3 � 3 sharpening attack on the test imageinduced by the StirMarkBenchmark 4.0 (Petitcolas et al.,2007).

� Histogram equalizationHistogram equalization is a contrast enhancement scheme.The objective of the scheme is to attain a new enhancedimage with a uniform histogram. The normalized cumula-tive histogram is regarded as the gray scale mapping func-tion when realizing the histogram equalization.

� CroppingWe applied surround cropping on the test image.

� Gaussian noisingWe applied Gaussian noising on the test image with(mean, variance) = (0, 20).

0.7

0.75

0.8

0.85

0.9

0.95

1

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

attack index

attack index

aver

age

BC

Rs

Our Scheme SVMLIW Shieh Chen Chang Sang

0.7

0.75

0.8

0.85

0.9

0.95

1

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

BC

R


a

b

Fig. 11. The performance of the proposed method in comparison with other existing metaverage BCR for six images; (b)–(g) the comparison result in terms of the BCR for Lena,

� Salt and pepper noisingWe applied salt and pepper noising on the test image with5% noise.

� PaintingSome letters (for example, ‘NFU’) are painted on a water-marked image to yield a degraded-and-watermarked image.

The proposed method includes three parameters, a value in thea-trimmed mean algorithm in (7), the threshold h in (9), and theblock width v of input vectors of training patterns in U in (10).First, for the case of Cropping, Fig. 10a displays the results whenthe a value decreases the average BCR value decreases. Note thatthe average BCR indicates the average of six BCR values for siximages. Observing Fig. 10a, when the a value is in [47.5%, 40%], bet-ter performance of a-trimmed mean algorithm can be obtained.Therefore, the a value is set to 47.5% in the proposed method. Sec-ond, Fig. 10b exhibits the results for various thresholds h. Obvi-ously, a maximum average BCR can be obtained when h is set to3. Finally, the results of the computer simulation, as shown inFig. 10c, are employed to determine a block width v to constructthe training patterns in U or U0. Observing Fig. 10c, the estimationaccuracy of the trained SVM is good when the block width v isgreater than or equal to 52. Consequently, the block width v isset to 52 in this proposed paper.

Fig. 11 displays the performance of the proposed method com-pared with that of other existing methods including the SVMLIW(Tsai et al., 2007), Shieh et al.’s (2005), Chen et al.’s (2005), Chang

16

attack index

attack index

0.7

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0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

BC

R


0.7

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R


c

d

hods for using different examined images (a) the comparative result in terms of theBaboon, Barbara, Peppers, Couple, and Streambridge, respectively.

0.7

0.75

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0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

BC

R


0.7

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0.85

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0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

BC

R


0.7

0.75

0.8

0.85

0.9

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1

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

BC

R


eg

f

Fig. 11 (continued)


and Lin’s (2008), and Sang and Alam’s methods (2005). The perfor-mance index is the average BCR value of six BCR values for six testimages. In the attack-free case, the proposed method is superior tothe four methods. Moreover, in all attacks, the performance of theproposed method is better than that of the four methods. Particu-larly, the proposed method can significantly outperform the fourmethods for some attacks, such as median filtering, histogramequalization, surrounding cropping, salt-pepper noising, and paint-ing. Figs. 12–17 show the visual perception comparison results ofusing the proposed method and the three methods for six testimages.

The robustness of the proposed method is superior to othermethods because it integrates the a-trimmed mean algorithmand the SVM. Although the a-trimmed mean algorithm is used toremove noises in the proposed method, it still cannot resist onsome attacks. Therefore, the SVM with well generalization abilityis employed in the design of the proposed method for memorizingthe relationship between feature information and the watermark(owner’s signature).

Fig. 18 exhibits the results to illustrate the trained SVM pos-sesses acceptable generalization ability to correctly estimate thewatermark bits even some error bits existing in the input vectors.Non-SVM in Fig. 18 represents the proposed method without SVM,

which retrieves watermark from feature information N0 on thecompletion of the FS component. Each N0 is collected by the corre-sponding attacked image. The error bit rate for N0 in these casesranges from 0% to 13.8%. For example, in Fig. 18a, the BCR is 94%while using the non-SVM method that extracts watermark fromthe test image Lena attacked by JPEG compression-attack (index1 in Table 1). Similarly, the BCR is 97% while using the non-SVMmethod that extracts watermark from the test image Lena attackedby Median Filter-attack (index 4). Note that the BCRs of Median Fil-ters 3 � 3 and 5 � 5-attack (indices 2 and 3) are better than that ofMedian Filter 7 � 7-attack (index 4). The BCR almost reaches to100% only when Brighten-attack (index 10) and Darken-attack (in-dex 11) are applied on the test image Lena. Observing Fig. 18, theaccuracy of watermark estimation can be improved by using thetrained SVM. It indicates that SVM has high accuracy rate of classi-fication. Therefore, a trained SVM can be used as an excellent toolto memorize relationship between the watermark and the image-dependent watermark.

Fig. 19 exhibits the results TBCR that the test input vector T 0k oftest pattern U0 compares with the training input vector Tk of train-ing pattern U in BCR

TBCR ¼ BCRðT 0k; TkÞ: ð12Þ

Lena

Attacks Our Scheme SVMLIW Chang Chen Sang Attacks Our Scheme SVMLIW Chang Chen Sang

Attack-free Scaling

JPEG 10% Blurring

Median 3 3 Brighten

Median 5 5 Darken

Median 7 7 Sharpen

Noising 5%

HistogramEqualization

Gaussian noise

Crop25%

Salt-pepper noise Painting

Fig. 12. Extracted watermarks using the proposed method and other methods for Lena.

Baboon

Attacks Our Scheme SVMLIW Chang Chen Sang AttacksOur

Scheme SVMLIW Chang Chen Sang

Attack-free Scaling

JPEG 10% Blurring

Median 3 3 Brighten

Median 5 5 Darken

Median 7 7 Sharpen

Noising 5%


Gaussian noise

Crop25%

Salt-pepper noise

Painting

Fig. 13. Extracted watermarks using the proposed method and other methods for Baboon.


The purpose of the experiment is to show that the testing patternscontain error bits instead of having no error bits. Error bit rate of T 0kis in [0%, 12.5%] for the six test images manipulated by these at-

tacks. The attacks, SaltPepper noising (index 7), Sharpen (index12), and Painting (index 15), cause large error bit rates. The highesterror bit rate reaches 12.5% for Baboon image because it has more

Barbara


Attack-free Scaling

JPEG 10% Blurring

Median 3 3 Brighten

Median 5 5

Darken

Median 7 7 Sharpen

Noising 5%


Gaussian noise

Crop25%

Salt-pepper noise

Painting

Fig. 14. Extracted watermarks using the proposed method and other methods for Barbara.

Peppers Attacks Our Scheme SVMLIW Chang Chen Sang Attacks Our Scheme SVMLIW Chang Chen Sang

Attack-free Scaling

JPEG 10% Blurring

Median 3 3

Brighten

Median 5 5

Darken

Median 7 7

Sharpen

Noising 5%


Gaussian noise

Crop25%

Salt-pepper noise

Painting

Fig. 15. Extracted watermarks using the proposed method and other methods for Peppers.


complex image features which reduce the performance of a-trimmed mean operator. The experimental results in Figs. 18 and19 reflect that the proposed method is powerfully robust due toemploying the SVM that possesses better generalization ability.The SVM is helpful to promote the BCR for watermark extraction.

5. Conclusions

The paper has proposed an effective zero-watermarking,based on the a-trimmed mean algorithm and the SVM, for imagecopyright protection. The proposed method uses the a-trimmed

Couple


Attack-free Scaling

JPEG 10% Blurring

Median 3 3 Brighten

Median 5 5 Darken

Median 7 7 Sharpen

Noising 5%


Gaussian noise

Crop25%

Salt-pepper noise

Painting

Fig. 16. Extracted watermarks using the proposed method and other methods for Couple.

Streambridge


Attack-free Scaling

JPEG 10% Blurring

Median 3 3 Brighten

Median 5 5 Darken

Median 7 7 Sharpen

Noising 5%


Gaussian noise

Crop25%

Salt-pepper noise

Painting

Fig. 17. Extracted watermarks using the proposed method and other methods for Streambridge.


mean algorithm to remove noises so as to enhance the accuracyof the feature selection procedure. Moreover, it employs thetrained SVM to effectively memorize the relationship betweenthe feature information (signal-dependent signature) and thewatermark (owner’s signature). Experimental results show that

the proposed method is definitely robust to resist commonimage-processing attacks and also outperforms other exist-ing methods under consideration. Thus it is a suitable approachof resolving the rightful ownership of medical and artisticimages.

(b)(a)

(d)(c)

(f)(e)

Fig. 18. The results reflect that the SVM possesses the generalization ability for six images.

0.85

0.9

0.95

1

1 4 5 6 7 8 9 10 11 12 13 14 15

attack index

TB

CR

Lena Baboon Barbara Peppers Couple Streambridge

Fig. 19. The TBCR between T 0k and Tk.


Acknowledgments

Authors would like to thank the National Science Council of Tai-wan, ROC, for financially supporting this research under ContractNo. NSC 96-2221-E-150-062. Our gratitude is extended to theanonymous reviewers for their valuable comments and profes-sional contributions to the improvement of this paper.

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Petitcolas, F., Anderson, R.J., Kuhn, M.G., 2007. StirMark: a library for StirMarkBenchmark 4.0, http://www.petitcolas.net/fabien/watermarking/stirmark/(retrieved 27.03. 07).

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Hung-Hsu Tasi received the B.S and the M.S degrees in applied mathematics fromNational Chung Hsing University, Taichung, Taiwan, in 1986 and 1988, respectively,

and the Ph.D. degree in computer science and information engineering fromNational Chung Cheng University, Chiayi, Taiwan, in 1999. Currently, he is a pro-fessor at Department of Information Management, National Formosa University,Huwei, Yulin, Taiwan. He has worked in industry for the SYSTEX Corporation, and inacademia for Nanhua University, Chiayi, Taiwan. He is an honorary member of thePhi Tau Phi Scholastic Honor Society. He has been selected and included in the 9thedition of Who’s Who in Science and Engineering which has been published in2006. He serves as a technical reviewer for various scientific journals and numerousinternational conferences. His research interests include computational intelli-gence, machine learning, support vector machines, multimedia security, intelligentfilter design, data mining, and system integration with web services.

Hou-Chiang Tseng received the B.S and the M.S degrees in Information Manage-ment from National Formosa University, Huwei, Yulin, Taiwan, in 2006 and 2008,respectively. He is a programmer at National Taiwan Normal University, Taipei,Taiwan. His research interests include support vector machine, digital water-marking and data mining.

Yen-Shou Lai received the B.S and the M.S degrees in information and computereducation from National Pingtung University of Education and National Universityof Tainan, Taiwan, respectively, and the Ph.D. degree in computer science andinformation engineering from National Chung Cheng University, Taiwan, in 2009.Currently, he is a teacher at Shin-Guang Elementary School, Yulin, Taiwan. Hiscurrent research interests are multimedia systems, computational intelligence, andcomputer-assisted learning.

http://www.petitcolas.net/fabien/watermarking/stirmark/

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