Feature based watermarking using watermark template match

Applied Mathematics and Computation 177 (2006) 377–386

www.elsevier.com/locate/amc

Feature based watermarking using watermark template match

Wei Lu a,b,*, Hongtao Lu a, Fu-Lai Chung b

a Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200030, Chinab Center for Multimedia Signal Processing and Department of Computing, Hong Kong Polytechnic University, Hung Hom,

Kowloon, Hong Kong, China

Abstract

This paper presents a robust digital image watermarking scheme using feature point detection and watermark templatematch. A scale interactive model based filter is used to extract the feature points of original image, based on which a water-mark template is constructed and embedded adaptively into the local region of these points. Watermark decision is madeby computing the statistical correlation between the watermark and the embedded region. Because the proposed featuredetection is robust against JPEG compression, filtering, noise addition and geometric distortions, the proposed water-marking scheme can achieve good performance against these attacks, and experimental results also demonstrate thesuperiority.� 2005 Elsevier Inc. All rights reserved.

Keywords: Digital watermarking; Feature detection; Template match

1. Introduction

Because of the great advance and convenience in digital media technology, digitized information is beingdistributed widely and fast through network, such as Internet. On the other hand, these digital contents arealso confronted with illegal or purposive handling, since these activities are easy and naked. As a result, con-tributors cannot protect their own legitimate rights and interests. So it is in great need of protecting the copy-right of digital contents. These years, digital watermarking technique is paid more attentions to as an effectiveapproach to resolve these serious issues mentioned above. Generally, to a watermarking system, a well con-structed watermark is embedded into the original data on condition that the quality degradation of the water-marked data is imperceptible. Furthermore, the watermark should be detected from the protected data, or thecorrect decision should be made on the existence of watermark or not, even when the watermarked data isattacked using different kinds of attacks and other artifices.

0096-3003/$ - see front matter � 2005 Elsevier Inc. All rights reserved.

doi:10.1016/j.amc.2005.11.015

* Corresponding author. Address: Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200030,China.

E-mail addresses: [email protected] (W. Lu), [email protected] (H. Lu), [email protected] (F.-L. Chung).

mailto:[email protected]



378 W. Lu et al. / Applied Mathematics and Computation 177 (2006) 377–386

Based on the difference of watermark carriers, there are three main watermarking techniques, digital audiowatermarking, digital image watermarking and digital video watermarking, the most popular one of which isdigital image watermarking, since image copyright protection is more urgent and is also the basic of digitalvideo watermarking. In this paper, we concentrate on digital image watermarking. Generally, a watermarkingalgorithm should provide with the following properties.

Robustness: Since watermarking is designed as a way to authenticate the copyright of the input data, it mustbe able to resist different kinds of intentional or unintentional watermarking attacks.

Imperceptibility: Watermarked data should be acceptable perceptually, i.e., there is no perceptual differencebetween the original data and the watermarked data.

Security: Watermark construction or detection is not arbitrary to anyone. Only the authorized user can exe-cute the corresponding processes.

Since the concept of watermarking is put forward in the early 1990s, it has been extensively studied in recentyears [1–5]. The first research wave of watermarking technique concentrates on the methods in spatial or trans-formation domain [6–9], which are robust against common signal processing attacks. Although some of themdemonstrate the robustness against geometric attacks, they need some additional techniques to recover theimage that are not introduced in these contributions. In recent years, many watermarking schemes have beendeveloped composed of other signal analysis methods, most of which can achieve the robustness against geo-metric attacks, such as template based watermarking [1,10] which, however, can also be attacked [11], affinetransformation invariant domain-based watermarking [12,13], where fourier mellon transformation and imagenormalization are popular, 2-D DFT domain-based circularly symmetric watermarking [14] and spatialdomain watermarking [15,16].

In this paper, we proposed a robust image watermarking using feature point detection and watermark tem-plate match. Some feature points are extracted using a scale interactive model based filter, which are robustagainst geometric distortions. A watermark is constructed based on these feature points, and then is embeddedinto the local regions of three points. The watermark is detected using statistical correlation between thewatermark and the corresponding embedding regions. The rest of this paper are organized as follows. In Sec-tion 2 the feature detection process is proposed using Hermite transformation based filter. In Section 3 water-mark template is constructed based on the location of the detected feature points. In Section 4 watermarkembedding procedure is introduced. In Section 5 watermark detection procedure is presented. In Section 6,the experimental results are given to demonstrate the superiority of the proposed scheme. Finally, conclusionsare given in Section 7.

2. Feature detection

Feature detection is a common technique for pattern recognition and analysis. In [3], Kutter et al. intro-duced the concept of the second generation watermarking, and a feature point detection based watermarkingis also developed, where a scale interactive model based filter is used for feature detection. Suppose that thespecified filter is F ð~xÞ, where ~x denotes the two-dimensional coordinate in time/spaital domain, the featuredetection filter can be constructed by
eF ð~xÞ ¼ jF ið~xÞ � kF jð~xÞj; ð1Þ where F ið~xÞ ¼ 2�i � F ð2�i �~xÞ is a scaled filter, i and j are integers and present the filter�s scaling factor, k is ascaling constant which ensures that the two scale filters have the same magnitude response at zero frequency,and eF ð~xÞ is the constructed filter. Obviously, the selection of the filter eF ð~xÞ is not arbitrary, and must satisfysome properties, which can be referenced in [17], where Mexican Hat wavelet is used as a specified filter. In thispaper, a 0th order Hermit Transformation Kernel based filter shown in Eq. (2) is applied to construct the fea-ture point detection filter. Since band-pass filters based on the Hermite Transformation are highly perceptuallyrelevant to the human visual systems, which are thought to be able to achieve a better watermarking combinedwith imperceptibility and feature detection.
F ð~xÞ ¼ d

dðk~xk=rÞ1

rffiffiffipp e�k~xk

2=r2

� �. ð2Þ

W. Lu et al. / Applied Mathematics and Computation 177 (2006) 377–386 379

Once the filter is constructed, the features can be detected by

I fð~xÞ ¼ Ið~xÞ � eF ð~xÞ; ð3Þ
where � denotes the convolution operator, I is the input host image and If is the output filtered image, which,as a feature enhanced image, need be processed to select the final feature points. Here, we select the local max-imum of If as the feature points and ensure that the distance between any two points is larger than a thresholdD. In our experiments, D is set 8 experimentally. Fig. 3(b) shows the detected feature points from the popularimage Lenna.
3. Watermark construction

In order to design a good watermarking, first of all, what we must consider is watermark construction, sincea good watermark can improve the watermark embedding capacity and the quality of watermarked image. Inour scheme, a zero mean binary sequence S composed of 1 and �1 is generated, then, a zero matrix W is setwith the same size as the original image. The sequence is placed one by one into the elements of the zero matrixW, where it is the feature points of the original image at same position, i.e., if~x is a feature point, W ð~xÞ is set toone element of the sequence S, where ~x is a two-dimensional coordinate. Thus the watermark W is con-structed, where each payload watermark element is corresponding to a feature point in the original image.

4. Watermark embedding

For the proposed watermark embedding, as a whole, each payload watermark element in W is embedded inthe local region of the corresponding feature point in the original image. The proposed procedure is shown inFig. 1 and can be described in detail as follows.

Suppose that the watermark element W ð~xÞ is embedded into the local region of the feature point~x. Firstly,in order to achieve local content dependent watermark embedding, the noise visibility function (NVF) pro-posed in [18] is used to improve the robustness and imperceptibility, which is defined by

að~xÞ ¼ 1� 1

1þ hr2l ð~xÞ

; ð4Þ

where r2l ð~xÞ is an unbiased estimation of the local variance over a square window with width l centered at~x,

and

h ¼ Dmaxð~xÞ

r2l ð~xÞ

; ð5Þ

where maxð~xÞr2l ð~xÞ is the maximum local variance and D 2 [50,100] is an experimental constant which is set to

75 in our experiments. Finally, the watermark element W ð~xÞ is embedded in the host image as

Iwð~xþ~rÞ ¼ Ið~xþ~rÞ þ að~xþ~rÞW ð~xþ~rÞ; ð6Þ

Fig. 1. The watermark embedding process.


where~r is a coordinate parameter which iterates over the square window region with width 3 centered at~x, i.e.,~r ¼ ð�1; 1Þ; ð0; 1Þ; ð1; 1Þ; ð1; 0Þ; . . . ; ð1;�1Þ. Once each watermark element is embedded into the neighborhoodregion of the corresponding feature point as the same manner described above, the watermarked image Iw isobtained.

5. Watermark detection

The watermark detection process illustrated in Fig. 2 can be divided into two steps, one is image recovering,the other is watermark correlation, which are discussed as follows.

5.1. Image recovering

As the input possibly watermarked image may be attacked, the extracted feature points are not correspond-ing with the watermark element in W exactly. Maybe some points cannot be detected and some new pointsappear, especially after geometric distortions. So the first step for watermark detection is to recovering theinput image into its original shape. The image can be recovered by

Idð~xiÞ ¼ I 0ðY �~x0i þ~tÞ; ð7Þ

where Y is a linear transformation matrix and~t is a translation vector, which map the coordinate of the inputimage into that of the recovered image. In order to obtain Y, some feature points are selected from the inputimage as~x0i, and their corresponding original position are~xi. Thus, Y can be computed using the method ofleast square as follow:

Me ¼ minX

i

ð~xi � Y~x0i �~tÞ2

( ); ð8Þ

where i denotes the number of selected points. In our experiments, i > 9, and the pair of feature points and thewatermark elements are selected manually in advance. It is obvious that the selection of feature points is veryimportant for the accuracy of image recovering, which will be discussed in later section.

5.2. Correlation detection

After the input image is recovered, the feature points can be re-extracted. Since the image has been pro-cessed and slight distortions may be unavoidable, the extracted feature points may be not corresponding withthe watermark elements exactly. If the distance between a watermark element and a extracted feature point isless than a threshold e, they are deemed to be corresponding. In our experiments, the threshold e is set to 2.Because each watermark element is embedded in the neighborhood region of each feature point as in Eq. (6),the watermark element is still corresponding with a watermarked point, even the point is not the original cor-responding feature point.

Fig. 2. The watermark detection process.


Suppose the extracted feature point is ~xi, firstly, the NVF is computed as a0ð~xiÞ, then watermark correlationcan be defined by

c ¼X

i

W ð~xiÞIdð~xiÞa0ð~xiÞ

� �; ð9Þ

where ~xi is a valid feature points, where there is a watermark element in the watermark W and a correspond-ing feature point in the recovered image. If I 0 is watermarked with W, i.e., Idð~xiÞ ¼ Ið~xiÞ þ að~xiÞW ð~xiÞ, wehave

c ¼X

i


� �¼X

i

W ð~xiÞðIð~xiÞ þ a0ð~xiÞW ð~xiÞÞa0ð~xiÞ

� �¼X

i

W ð~xiÞIð~xiÞa0ð~xiÞ

þ 1

� �¼ N þ

Xi


� �; ð10Þ

where N is the total number of valid feature points. If I 0 is watermarked with W 0, and W 5 W 0, we have

c ¼X

i


� �¼X

i

W ð~xiÞðIð~xiÞ þ a0ð~xiÞW 0ð~xiÞÞa0ð~xiÞ

� �¼X

i


þ W ð~xiÞW 0ð~xiÞ� �

. ð11Þ

If I 0 is not watermarked with any watermark, we have

c ¼X

i


� �¼X

i


� �. ð12Þ

Here, an assumption is made that the watermark and the host image is independent, i.e.,

EfI � W g ¼ 0 and EfW 0 � W g ¼ 0.

Thus, the mathematical exception of c can be computed as follows:

ce ¼ Efcg ¼

N if w ¼ w0;

0 if w 6¼ w0;

0 if non-watermark.

8>><>>: ð13Þ

Then, the decision on whether the watermark exists or not can be make by ce with a threshold T, i.e., if ce P T,the input image I 0 is watermarked with W, and if ce < T, it is not watermarked with W. Experimentally,T = N 0/3 in our experiments, where N 0 is the number of payload watermark element in W.

6. Experimental results

Experiments are conducted to demonstrate the robustness of the proposed watermarking scheme designed.Mostly, we use the popular image Lenna shown in Fig. 3(a) to test the proposed scheme, and Fig. 4(a) showsthe watermarked image. Fig. 4(b) shows the detected feature points from Fig. 4(a), where all the feature pointsare detected and watermark detection is also successful to declare the watermark�s existence.

JPEG compression was first used to test the proposed scheme, since it is the most popular image storageand compression standard. Fig. 6 shows the detected feature points under JPEG quality 80 and 40, wherethere are 215 detected feature points and the correlation detection is 205, which is much larger than N 0/3 = 72. Fig. 5 shows the correlation detection results under JPEG compression, where the watermark can still

Fig. 4. (a) The watermarked image. (b) Detected feature points (N = 218).

Fig. 3. (a) The original image. (b) Detected feature points (N 0 = 218).

0 20 40 60 80 1000

50

100

150

200

250

300

JPEG quality

Cor

rela

tion

coef

ficie

nt

Fig. 5. Correlation detection results under JPEG compression.


Fig. 6. (a) JPEG quality 80. (b) JPEG quality 40.

20 25 30 35 4080

100

120

140

160

180

200

220

240

PSNR(dB)

Cor

rela

tion

coef

ficie

nt

Gaussian lowpass filterGaussian noise addition

Fig. 7. Correlation detection results under Gaussian lowpass filter and Gaussian noise addition.


be detected even under JPEG quality 10, which demonstrates a strong robustness of the proposed schemeagainst JPEG compression.

Several common signal processing attacks were also applied to the watermarked image, including lowpassfiltering, Gaussian noise addition, salt-pepper noise addition and median filtering. Fig. 8 shows the detectedfeature points, the number of which is 208, 230, 301 and 199 separately, and the correlation detection are 202,197, 179 and 187 correspondingly. Fig. 7 gives the correlation detection results under Gaussian lowpass filterand Gaussian noise addition, which shows that the proposed scheme can achieve the good robustness to theseattacks.

Generally, geometric attacks involve geometric distortion attacks, which includes rotation, resizing andtranslation (RST), and others, such as cropping, pixel removing, row removing, etc. Fig. 9(a) shows the imagewith rotation angle 20�. Fig. 9(b) shows the recovered image, where 14 feature points are selected to recoverthe rotated image. Fig. 10 shows the relation between the correlated detection and the number of selected fea-ture points for recovering, where, when the number is larger than 5, the watermark detection can give the cor-rect detection result, and when it is larger than 7, the watermark detection is close to the mathematicalexpectation of the correlation detection. Table 1 gives some experimental results under geometric attacks,where the watermark can be detected correctly.

Fig. 8. (a) Gaussian lowpass filtering (208). (b) Gaussian noise addition (230). (c) Salt pepper noise (301). (d) Median filter (199).

Fig. 9. (a) Rotated image with angle 20�. (b) Recovered image.


0 5 10 15 200

50

100

150

200

250

Number of selected points

Num

ber

of d

etec

ted

wat

erm

ark

elem

ent

Fig. 10. Relation curve between the number of select recovering point and the number of detected watermark element.

Table 1Robustness experiments under geometric attacks

Attacks ce

Lenna Baboon

Rotation (20�) 212 300Rotation (80�) 213 301Rotation (�60�) 212 298Resizing (1.5) 217 290Resizing (1.2) 216 295Resizing (0.5) 209 287Resizing (1.2) + rotation (�60�) 205 291Resizing (0.8) + rotation (40�) 201 286Rotation (�60�) + resizing (1.2) 205 290Rotation (40�) + resizing (0.8) 202 286


7. Conclusions

In this paper, we have proposed a robust digital image watermarking scheme using feature point detectionand watermark template match, aiming at achieving the robustness against common signal processing attacksand geometric distortions. The key contribution of this paper are

• Watermark as a template is introduced to recover the input image. Although template based watermarkinghas been introduced much into watermarking, watermark and template is separated in most applications.

• Image recovering is simple by using a linear transformation matrix, the computation complexity isdecreased significantly.

Further work will concentrate on improving the performance of the watermarking system. Some consider-ation has been made as follows:

• The feature points selection for recovering is the key factor in our scheme, an algorithm-based selectionmethod will be thought to improve the veracity of recovering.

• Since the transformation domain watermarking is better than spatial domain watermarking naturally,improvement on watermark embedding algorithm will be helpful for the robustness.


Acknowledgements

This work is supported by NSFC under project no. 60573033, the ICRG grant under project A-PG49 andCenter for Multimedia Signal Processing of The Hong Kong Polytechnic University.

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Feature based watermarking using watermark template match