Rotation invariant watermark embedding based on scale-adapted characteristic regions

Information Sciences 180 (2010) 2875–2888

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Information Sciences journal homepage: www.elsevier.com/locate/ins

Rotation invariant watermark embedding based on scale-adapted characteristic regions Leida Li a,*, Xiaoping Yuan a, Zhaolin Lu a, Jeng-Shyang Pan b a b

School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung 807, Taiwan

a r t i c l e

i n f o

Article history: Received 13 November 2009 Received in revised form 1 March 2010 Accepted 9 April 2010

Keywords: Image watermark Geometric attack Rotation invariance Quantization

a b s t r a c t Rotation invariance is one of the most challenging issues in robust image watermarking. This paper presents two rotation invariant watermark embedding schemes in the non-subsampled contourlet transform (NSCT) domain based on the scale-adapted local regions. Watermark synchronization is achieved using the scale-space feature point based local characteristic regions. The first method embeds a binary watermark sequence by partitioning the local regions in a rotation invariant pattern. The second method embeds a binary watermark image in a content based manner, and the watermark signal is adaptive to the orientation of the image. Both methods achieve rotation invariant embedding by only using the rotation normalizing angle, thus no interpolation operation is performed. Extensive simulation results and comparisons show that the proposed schemes can efficiently resist both signal processing attacks and geometric attacks. Ó 2010 Elsevier Inc. All rights reserved.

1. Introduction Information system security has been an increasing important problem nowadays [15]. While digitized multimedia products have brought us great ease, they are more likely to be misused by possible attackers. As a supplementary technique to cryptology, digital watermarking has been regarded as a promising way to protect the copyright and integrity of such materials [3,5,9,10,26,29]. Typically, a watermarking system is inevitably subject to various kinds of attacks. These attacks can be classified into traditional signal processing attacks and geometric attacks. While image compression, filtering, enhancement consist of the most common signal processing attacks, geometric attacks are usually characterized by replacement of pixels in spatial domain. Signal processing attacks act on a watermarking system by reducing the watermark energy so that the extracted watermark is not recognizable or not sufficient enough to be used as the evidence. By comparison, geometric attacks act in a quite different way. After geometric transformations, the watermark still exists in the cover signal. However, the positions of the watermark have changed, so that traditional watermarking schemes cannot detect the watermark signal correctly. Rotation, scaling and translation (RST) are three of the most common geometric attacks which can be easily implemented in many image processing softwares. During the last few years, many algorithms have been proposed to deal with such attacks, and most of them fall into one of the following three categories [17,36]: (1) Inverse transform. Before watermark detection, the distorted image is first rectified to have the same form as the original image. Extensive search and template based methods belong to this category [16,22]. (2) Invariant domain embedding. Watermark embedding and extraction are implemented in a domain that is invariant to geometric attacks. General domains used in image watermarking include Fourier–Mellin transform [18,21,38], invariant moment [11,31,34,35] and image normalization [1,8]. (3) Feature based

* Corresponding author. Tel.: +86 150 0520 3739. E-mail addresses: [email protected], [email protected] (L. Li). 0020-0255/$ - see front matter Ó 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.ins.2010.04.009

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embedding. Salient features extracted from the image can be used as the reference signals to determine some local invariant regions. These regions can be used for both watermark embedding and extraction. This kind of scheme is often regarded as the second generation watermarking [12]. Recently, machine learning has also been incorporated into watermarking to resist geometric attacks, for example support vector machine (SVM) based watermarking [28]. The methods presented in this paper belong to feature based embedding. Due to its good invisibility and generalized robustness, feature based image watermarking has become a hot research field. In Lin’s method, local features are extracted by finding the perceptually prominent coefficients in wavelet domain, based on which watermarking and cryptography are then combined to protect digital images [19]. Recently, many feature based image watermarking schemes have been proposed based on local invariant feature points [2,7,13,14,23– 25,27,30,32,37]. Among them, scale-space feature points have been widely employed in these schemes due to their invariance to geometric transformations. Usually, local characteristic regions are extracted and used for watermark synchronization. As the regions are scale adapted, they can cover the same content even the image is distorted by scaling. However, how to achieve rotation invariant watermark embedding based on these regions is still one of the most challenging issues. In Tang’s method, image normalization is employed to achieve rotation invariance [27]. In the literature [24], while rotation invariance can be achieved by embedding the watermark in a circularly symmetric way, cyclic convolution has to be first conducted to formulate a hypothesis testing during watermark detection. In another scheme proposed by Seo, the watermark is embedded after geometric normalization according to the shape of the region. Resilience against rotation is achieved by the characteristic orientation, which is computed using the principal axes moments [25]. In Wang’s method, the rotation attack should be first rectified using the magnitude of Fourier spectrum [30]. However, the rectification process will inevitably introduce interpolation error, which can be seen as a kind of signal processing attack before watermark detection. In Lee’s method, the 2-D rectangular watermark is first polar mapped into circular shape before spatial domain embedding [13]. In this way, rotation invariance is achieved using the translation invariant property of the polar mapped circular patches. During watermark extraction, circular convolution is done between the reference watermark and the retrieved one. Then the response of the watermark detector is represented by the maximum value of the convolution. More recently, Yang et al. present a new feature based image watermarking scheme in the non-subsampled contourlet transform (NSCT) domain [32]. The watermark is embedded into some local regions extracted from the original image. During embedding, the initial region is divided into some cirque subregions with the same area, and each watermark bit is embedded into one sub-region. This method has been shown to be efficient in resisting both signal processing attacks and geometric attacks. In this paper, we present two rotation invariant watermark embedding schemes based on the scale-adapted local characteristic regions. Watermark synchronization is first achieved using the scale-invariant feature transform (SIFT) [20]. In both methods, the watermark is embedded in the NSCT domain [6]. The first method first partitions the local region and embeds a binary watermark sequence into the fan-shaped subregions. The partition itself is rotation invariant because the start radius is determined by the rotation normalization angle, which will be introduced in Section 2. The second method embeds a high capacity binary watermark image into the local regions in a content based manner. Besides, the embedded watermark is adaptive to the orientation and scale of the local regions. While information hiding in transform domain using feature point has been addressed in the literatures [4,30], the proposed scheme is characterized by high capacity and generalized robustness. Extensive simulation results and comparisons have demonstrated the efficiencies of the proposed schemes. 2. Geometric attacks in image watermarking An image watermarking system can be modeled as a communication channel, where the original image is the carrier signal and the watermark is the transmitted signal. In order to recover the transmitted signal, the synchronization has to be achieved first. Watermark extraction from the distorted image is just like recovering a signal from the communication channel. Synchronization is of great importance. This problem is most pronounced when the watermarked image is subject to geometric attacks.

Fig. 1. Geometric attacks in image watermarking. (a) Original image, (b) rotated image, and (c) scaled image.

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Fig. 1 illustrates how geometric attacks act on an image watermarking system with image rotation and scaling as the examples. Assume that a watermark signal is embedded in the position (x0, y0), it is straightforward to know from Fig. 1(b) and (c) that the watermark positions have changed to (x1, y1) and (x2, y2), respectively, in the rotated and scaled images. However, traditional watermarking schemes also try to retrieve the watermark from the absolute coordinate (x0, y0). As a result, the watermark cannot be detected correctly. In other words, the synchronization between watermark embedding and extraction is destroyed. Watermark synchronization is a process to guarantee that watermark embedding and detection are implemented in the same place of the image. In this paper, watermark synchronization is achieved using the scale-space feature point based local circular regions. Specifically, the method in the literature [13] is employed to generate the local regions for watermark embedding and extraction. Fig. 2 shows an example of the local circular regions generated on the standard image Lena. It is easily seen that the regions have different sizes. This is because the radius is determined by the characteristic scale associated with the feature point. These regions are scale invariant so that they can cover the same image content even though the image is scaled. Further details on how to generate these regions can be found in the literature [13]. Embedding a watermark into the above scale-adapted local regions is still a challenging issue, because the image may be subject to rotation attacks. The most straightforward way to deal with rotation attack is to apply rotation normalization. However, the interpolation will degrade the image quality during watermark embedding and cause inaccurate extraction at the watermark decoder side. In this paper, we present two rotation invariant watermark embedding schemes by only using the rotation normalization angles. Image normalization is not actually implemented during watermark embedding and extraction. Image normalization is the technique to transform an image into another one that has standard size and orientation [1]. Given an image f(x, y), its (p + q) th order geometric moment mpq is defined as

mpq ¼

XX x

xp yq  f ðx; yÞ

ð1Þ

y

The corresponding (p + q) th order central moment lpq is

lpq ¼

XX Þq  f ðx; yÞ ðx  xÞp ðy  y x

ð2Þ

y

Þ is the centroid of the image. For rotation normalization, two tensors t1 and t2 are first computed. where ð x; y

t1 ¼ l12 þ l30 ;

t2 ¼ l21 þ l03

ð3Þ

Then the rotation normalizing angle h is defined as

h ¼ arctanðt 1 =t 2 Þ

ð4Þ

It is obvious that there are two solutions to Eq. (4), say h and h + p. In order to obtain a unique angle, another tensor t3 can be defined.

t3 ¼ t 1 sin h þ t2 cos h

ð5Þ

For h and h + p, only one can satisfy t3 > 0. As a result, we obtain a unique angle h by making t3 > 0. If t3 < 0, then h = h + p. Given the normalization angle h, the image can be normalized by rotating it clockwise by angle h. In this paper, the local circular regions are scale-adapted so that they cover the same image content. Therefore, the difference between the rotation normalization angles should be equal to the actual rotation angle. In order to test the computation accuracy of the rotation normalization angle, a region is selected from Fig. 2 and distorted by rotations with angles ranging from 10° to 90°. Then the normalization angle is computed for each of the distorted image. We also compute the difference between the actual rotation angle and the difference of the normalization angles. Simulation results are given in Table 1 and Fig. 3.

Fig. 2. Characteristic regions for watermark synchronization.

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Table 1 Computation accuracy of rotation normalization angle. Rotation angle (°)

0

10

20

30

40

N

Y

N

Y

N

Y

N

Y

N

Y

Normalizing angle (°) Difference

282.76 0.00

283.21 0.45

272.37 0.39

271.89 0.87

261.84 0.92

261.87 0.89

251.56 1.20

252.01 0.75

241.02 1.74

241.48 1.28

Rotation angle (°)

50

Normalizing angle (°) Difference

60

70

80

90

N

Y

N

Y

N

Y

N

Y

N

Y

231.71 1.05

231.47 1.29

222.36 0.40

221.71 1.05

212.73 0.03

213.07 0.31

203.57 0.81

203.8 1.04

193.24 0.48

193.34 0.58

Fig. 3. Difference between the rotation angle and the difference of normalization angles.

In Table 1, ‘‘N” denotes the image without watermark embedded, and ‘‘Y” denotes the watermarked image. It can be seen from the table that the watermarking process will introduce computation error of the normalization angle, so that the error is not zero even the watermarked image is not rotated. It can be seen from the simulation results that the difference between the rotation normalization angles can represent the actual rotation angle with high accuracy. Based on this finding, we propose two rotation invariant watermark embedding schemes, which will be described in the next section. 3. Proposed watermarking schemes The proposed two schemes embed the watermark in a rotation invariant pattern in the NSCT domain. The first method is a partition based method, which embeds a binary watermark sequence into the fan-shaped subregions. The second method embeds a binary watermark image into the local regions adaptively. 3.1. Partition based embedding In this subsection, we present a rotation invariant watermark embedding scheme by first partitioning the circular regions. In order to embed a watermark sequence, we propose to partition the region into multiple fan-shaped subregions with the same number of the watermark length. Assume that the N-bit long watermark sequence is W = {w1, w2, w3, . . ., wN}, wi 2 {0, 1}, the circular region is first partitioned into N fan-shaped subregions with the same area. Fig. 4 shows how the subregions (SR) can be obtained. It should be noted that when rotation occurs, the circular regions have different orientations. As a result, the partition itself should be rotation invariant. In this paper, the start radius of the partition does not begin from the horizontal line with angle 0°. Instead, the first subregion is determined according to the rotation normalization angle. For the watermark sequence W = {w1, w2, w3, . . ., wN}, the angle of each subregion is 2p/N. In order to describe the subregion, the Cartesian coordinate (x, y) of the region is first transformed into polar system (qx,y, hx,y) as follows:

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Fig. 4. Partition of the circular region.

qx;y ¼

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi x2 þ y 2 ;

hx;y ¼ arctanðy=xÞ

ð6Þ

If the rotation normalization angle of the local region is h, then each SR can be described as follows:

SRi ¼

  2p 2p ðx; yÞj  h þ ði  1Þ 6 hx;y < h þ i N N

ð7Þ

where i = 1, 2, . . ., N. Fig. 5 shows an example of the rotation invariant partition of a local circular region. In this example, the watermark sequence is 6-bit long. The subregions are generated from the original, 30° rotated, 45° rotated and 90° rotated images, respectively. It can be seen from the figure that although their orientations are different, the content of the subregions with the same index are always the same. As a result, if the watermark is embedded into these subregions, they can be correctly detected with high accuracy. In the proposed scheme, the watermark is embedded in NSCT domain using odd–even quantization. Besides, a watermark bit wi is embedded into a SR with the same index, i.e. SRi. In implementation, all coefficients in the same SR are quantized into odd or even coefficients according to the watermark bit. First, the coefficient c(x, y) is assigned sign ‘‘0” or ‘‘1” using the quantization function.

Q ðx; yÞ ¼



0;

if kD 6 cðx; yÞ < ðk þ 1ÞD for k ¼ 0; 2; . . .

1; if kD 6 cðx; yÞ < ðk þ 1ÞD for k ¼ 1; 3; . . .

Fig. 5. Rotation invariant partition of the circular region.

ð8Þ

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where D is the quantization step. In order to enhance watermark robustness, the modified NSCT coefficient should locate at the middle of the corresponding quantization interval [33]. Therefore, we modify the coefficients as follows. The quantization noise is first computed using

rðx; yÞ ¼ cðx; yÞ 

  cðx; yÞ D D

ð9Þ

where bc is the floor operation. Then the amount of modification is

8 > < rðx; yÞ þ 0:5D; if Q ðx; yÞ ¼ wi uðx; yÞ ¼ rðx; yÞ þ 1:5D; if Q ðx; yÞ – wi ; > : rðx; yÞ  0:5D; if Q ðx; yÞ – wi ;

rðx; yÞ > 0:5D

ð10Þ

rðx; yÞ 6 0:5D

Then the modified NSCT coefficient c0 (x, y) is obtained.

c0 ðx; yÞ ¼ cðx; yÞ þ uðx; yÞ

ð11Þ

The modified coefficients are reconstructed to obtain the watermarked region, which is then used to replace the original region. The above operation is done circularly for all regions, producing the whole watermarked image. During watermark detection, the local circular regions are first redetected from the test image. Then the region is transformed into NSCT domain and fan-shaped subregions are obtained using the same method as embedding. One watermark bit can be detected from each subregion. For a circular region, it is first transformed into NSCT domain. Then it is divided into N fan-shaped subregions using Eq. (7), and the watermark can be detected as follows. The coefficients inside SRi are first assigned number ‘‘0” or ‘‘1” using Eq. (8). The number of ‘‘0” coefficients is denoted by Numi,0, and the number of ‘‘1” coefficients is denoted by Numi,1. Then the watermark bit can be extracted as follows:

w0i

¼

8 < 0; if : 1; if

Numi;0 Numi;1

P1

Numi;1 Numi;0

>1

ð12Þ

  The whole extracted watermark W 0 ¼ w01 ; w02 ; w03 ; . . . ; w0N can be obtained by combining all the bits. At last, the normalized hamming similarity (NHS) between the extracted watermark W0 and original watermark W is computed to decide the presence of the watermark.

NHS ¼ 1 

HDðW; W 0 Þ N

ð13Þ

where HD(, ) denotes the number of bits different in the two binary watermark sequences, and N is the number of watermark bits. 3.2. Content based embedding In the last section, a novel rotation invariant image watermarking has been presented to embed a binary watermark sequence. A potential deficiency of the scheme is that the watermark capacity is limited, typically dozens of bits. In this section, we present a high capacity watermarking scheme, which aims to embed a binary watermark image. The watermark is embedded in a content based manner by using the rotation normalization angle as the reference. The diagram of the proposed watermark embedding scheme is shown in Fig. 6. In the proposed scheme, the watermark is a circular binary watermark image. If for some reason, watermark with other shapes should be used, its circumcircle image can be used as the watermark image. For the original image, local regions are first generated. Then the rotation normalization angle is computed for each region. Before embedding, the original watermark image is first adjusted with respect to the orientation and size. In implementation, the watermark is first rotated clockwise by the rotation normalization angle. Then the rotated watermark image is rescaled to have the same size as the local circular region. Fig. 7 shows an example of the local region and the corresponding watermark image to be embedded, where the number denotes the rotation normalization angle. It can be seen from Fig. 7 that unlike traditional watermarking algorithms which embed a uniform watermark signal, the proposed method embeds watermark images with different orientations and sizes into different local regions. In other words, the watermark signal is adaptive to the local region. Besides, the watermark is embedded in NSCT domain. The NSCT is a flexible multiscale, multidirection, and shift-invariant version of the Contourlet transform. We employ the NSCT in that it produces subband images with the same size with the original image. As we use small local circular regions to embed the watermark, NSCT will produce more coefficients than discrete wavelet transform (DWT) or other transforms. This is crucial for high capacity watermark embedding. Besides, it is a redundant transform. Therefore, it is a quite an efficient tool for information hiding. Fig. 8 illustrates the detailed watermark encoding process. For an individual region, it is first decomposed by NSCT. Then the low frequency subband coefficients are employed to embed the watermark. As the watermark image have the same size as the local region, a watermark bit is encoded by modifying one NSCT coefficient that has the same coordinate. Let the

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Fig. 6. Diagram of watermark embedding.

Fig. 7. Local region and the embedded watermark.

Fig. 8. Local region and the embedded watermark.

watermark image be denoted by W, to embed the watermark bit w(x, y), the NSCT coefficient at position (x, y) is first selected. Then w(x, y) is embedded by quantization. The embedding equation is the same as the partition based method in Section 3.1.

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Fig. 9. Watermark rectification.

The difference is that a watermark bit is encoded by modifying many coefficients in a fan-shaped subregion in Section 3.1, while this method embeds a watermark bit by modifying only one coefficient with the same position. When all the watermark bits are embedded, inverse NSCT is performed to obtain the watermarked local region. Finally, the whole watermarked image can be obtained by replacing all the local regions with the watermarked ones. During watermark extraction, a region is first transformed into NSCT domain and the low frequency subband coefficients are selected. Then the watermark image can be extracted using Eq. (8). It should be noted that the watermark is embedded adaptively to the orientation of the region. The orientation of the extracted watermark is not same to the original watermark image. The irregularity of the orientation can be rectified by referring to the rotation normalization angle. Fig. 9 shows the process of watermark rectification. For a test image, the watermark image is first extracted from the local circular region. Then the watermark is rectified by using the rotation normalization angle. If the normalization angle is h, the extracted watermark image is then rotated anti-clockwise by h degree. As the watermark image is rotated clockwise by h degree before embedding, this operation will compensate for the orientation error. It should be noted that as h may be positive or negative, the actual rectification operation may be in anti-clockwise direction or clockwise direction. In other words, if h is positive, the extracted watermark image is rotated anti-clockwise, while clockwise for negative angles. Finally, the extracted watermark is scaled back to the original size and the NHS is computed to determine the presence of the watermark. Furthermore, the final decision can also be made by judging whether the extracted watermark image is recognizable.

Fig. 10. Watermark invisibility. (a) Original images, (b) watermarked images using the first method, and (c) watermarked images using the second method.

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4. Experimental results In this section, we estimate the performance of the proposed schemes by doing various kinds of experiments. In the first method, the watermark sequence is 32 bit long. For the second method, the size of the watermark image is 160  160. Fig. 10 shows the results of watermark embedding on standard images Lena, Peppers and Baboon. The peak signal-to-noise ratio (PSNR) is employed to evaluate the image quality, which is defined as:

2552 MSE W1 H1 XX

PSNR ¼ 10log10 MSE ¼

1 W H

i¼0

ð14Þ ½f ði; jÞ  f 0 ði; jÞ

2

ð15Þ

j¼0

where f and f0 denote the original image and watermarked image, respectively, both with size W  H.

1.3

Smax with watermark

1.2

S

without watermark

S

with watermark

max

mean

1.1

Smean without watermark

Similarity

1 0.9 0.8 0.7 0.6 0.5 0.4

0

20

40 60 Rotation angle (degree)

80

100

Fig. 11. Watermark robustness to rotation attacks.

1

0.9

S

with watermark

S

without watermark

S

with watermark

max

Similarity

0.8

max

mean

Smean without watermark

0.7

0.6

0.5

0.4

0.6

0.8

1 1.2 Scaling factor

Fig. 12. Watermark robustness to scaling attacks.

1.4

1.6

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The PSNR values of the three images are 41.328 dB, 42.274 dB, 42.892 dB for the first method, and 41.189 dB, 42.464 dB, 42.990 dB for the second method. The PSNR values are very high because watermark is embedded into the local regions of the image instead of the whole image. It can also be seen from Fig. 10 that the embedded watermark is invisible to the naked eyes. To test the robustness of the proposed schemes, we have applied various attacks on the watermarked images, including traditional signal processing attacks and geometric attacks. Traditional attacks include added noise, median filtering, JPEG compression, etc., and geometric attacks consist of rotation, scaling, cropping, etc. Generally, a watermark can be extracted from each of the local regions. In this paper, we estimate the performance by evaluating the maximum similarity and the mean similarity, which are denoted by Smax and Smean, respectively. Assume K watermarks are extracted from K local regions, then Smax and Smean are defined as:

1

0.9

S

with watermark

S

without watermark

max max

Smean with watermark

Similarity

0.8

Smean without watermark 0.7

0.6

0.5

0.4

0

20

40 60 Quality factor

80

100

Fig. 13. Watermark robustness to JPEG compression.

Table 2 Watermark robustness to attacks. Attacks

Gaussian noise Med-filter (3  3) JPEG 30 JPEG 50 JPEG 70 Rotation 10° Rotation 30° Rotation 45° Rotation 60° Rotation 90° Rotation 10° + cropping Rotation 15° + cropping Rotation 30° + cropping Rotation 45° + cropping Rotation 60° + cropping Scaling 0.6 Scaling 0.8 Scaling 1.2 Scaling 1.4 Translation x-40 + y-40 Rotation 10° + scaling 0.9 Rotation 30° + scaling 0.9

Lena

Peppers

Baboon

Proposed

Yang et al. [32]

Proposed

Yang et al. [32]

Proposed

Yang et al. [32]

0.969 1.000 0.969 1.000 1.000 0.969 0.969 0.906 0.906 0.969 1.000 1.000 0.969 0.969 0.938 0.844 1.000 1.000 1.000 1.000 1.000 0.906

0.750 0.875 0.844 0.844 0.969 0.969 0.875 0.719 0.656 0.594 0.906 0.906 0.750 0.781 0.594 0.750 0.906 0.906 0.750 0.969 0.750 0.781

0.969 1.000 0.938 1.000 1.000 1.000 0.969 0.969 0.969 0.969 1.000 1.000 1.000 1.000 0.969 0.938 1.000 1.000 1.000 1.000 1.000 0.969

0.813 0.906 0.875 0.906 1.000 0.938 0.938 0.906 0.844 0.813 0.938 0.938 0.938 0.875 0.906 0.844 0.938 0.938 0.906 0.938 0.938 0.906

0.875 0.938 0.813 0.969 1.000 1.000 1.000 1.000 0.938 0.813 1.000 1.000 1.000 1.000 0.906 0.750 0.938 1.000 0.875 1.000 0.969 0.969

0.719 0.844 0.719 0.844 0.875 0.938 0.906 0.781 0.813 0.813 1.000 0.906 0.875 0.906 0.750 0.656 0.781 0.875 0.656 0.969 0.750 0.688

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Smax ¼ maxðNHS1 ; NHS2 ; . . . ; NHSK Þ 1 Smean ¼ ðNHS1 þ NHS2 þ    þ NHSK Þ K

ð16Þ ð17Þ

Figs. 11–13 show the maximum and mean watermark similarities under image rotation, scaling and JPEG compression. For rotation attack, the watermarked image is rotated from 10° to 90° with the interval of 10°. For scaling, the watermarked image is rescaled with factors ranging from 0.6 to 1.5 before watermark detection. For JPEG compression, the watermarked image is compressed with quality factors from 10% to 100%. For comparison purposes, the same attacks are also applied on the unwatermarked images. Then the watermark detection procedure is also performed on these distorted images and the watermark similarities are also illustrated in the figures. It can be seen that the watermark similarities computed from the watermarked images are all higher than those computed from the unwatermarked images. What is more, even

0.9 Smax with watermark

0.85

S

without watermark

S

with watermark

max

0.8

mean

Smean without watermark

Similarity

0.75 0.7 0.65 0.6 0.55 0.5 0.45 0.4

0

20

40 60 Rotation angle (degree)

80

100

Fig. 14. Watermark robustness to rotation attacks.

0.9 S

with watermark

S

without watermark

max

0.85

max

Smean with watermark

0.8

S

mean

Similarity

0.75

without watermark

0.7 0.65 0.6 0.55 0.5 0.45 0.4

0.6

0.8

1 1.2 Scaling factor

Fig. 15. Watermark robustness to scaling attacks.

1.4

1.6

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the mean watermark similarities of the watermarked images are higher than the maximum similarities of the unwatermarked images. Generally, the watermark similarities computed from the watermarked images are higher than 0.8, while the similarities computed from the unwatermarked images are lower than 0.7. As a result, it is reasonable to set a threshold

0.9 Smax with watermark

0.85

Smax without watermark

0.8

Smean with watermark S

Similarity

0.75

mean

without watermark

0.7 0.65 0.6 0.55 0.5 0.45 0.4

0

20

40 60 Quality factor

80

Fig. 16. Watermark robustness to JPEG compression.

Fig. 17. Distorted images and the extracted watermarks.

100

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L. Li et al. / Information Sciences 180 (2010) 2875–2888 Table 3 Watermark robustness to attacks. Attacks

Lee’s [13]

Proposed Lena

Med-filter (3  3) Gaussian noise JPEG 60 JPEG 70 JPEG 80 JPEG 90 Shearing x-0%, y-5% Shearing x-5%, y-0% Shearing x-1%, y-1% Row 17 and column 5 removal Row 5 and column 17 removal Rotation 5° + cropping Rotation 10° + cropping Rotation 15° + cropping Rotation 30° + cropping Rotation 45° + cropping Scaling 0.7 Scaling 0.9 Scaling 1.1 Scaling 1.3 Scaling 1.5 Centered cropping 5% Centered cropping 25%

0.629 0.532 0.579 0.623 0.660 0.698 0.417 0.389 0.645 0.555 0.558 0.641 0.600 0.538 0.514 0.550 0.472 0.605 0.606 0.594 0.524 0.703 0.621

Peppers

Smax

Smean

Smax

Smean

0.729 0.612 0.711 0.729 0.736 0.752 0.708 0.699 0.678 0.684 0.704 0.720 0.726 0.696 0.709 0.685 0.635 0.753 0.706 0.676 0.722 0.759 0.738

0.679 0.586 0.662 0.674 0.652 0.701 0.671 0.651 0.660 0.664 0.635 0.687 0.680 0.670 0.638 0.673 0.588 0.665 0.662 0.603 0.663 0.712 0.700

0.732 0.616 0.732 0.751 0.755 0.770 0.723 0.707 0.706 0.731 0.679 0.760 0.764 0.735 0.734 0.704 0.676 0.756 0.769 0.766 0.731 0.779 0.775

0.695 0.602 0.697 0.712 0.730 0.743 0.692 0.689 0.682 0.690 0.656 0.719 0.721 0.717 0.708 0.667 0.637 0.706 0.734 0.719 0.705 0.750 0.751

between 0.7 and 0.8 to determine the presence of the watermark. In this paper, the threshold is set to be 0.75. Table 2 lists the watermark similarities computed from the more generalized distorted images, where only the maximum similarities are given. For comparison, the simulation results of Yang’s method are also presented under the same experimental parameter settings, namely 32 bit long watermark embedding with the same embedding strength. It is observed from Table 2 that the similarities of the watermarked images are mostly higher than 0.9 so that we can say that the watermark can be successfully extracted. Comparisons also demonstrate that the proposed scheme outperforms Yang’s method. For the second method, the same experiments have also been done. The simulation results are shown in Figs. 14–16. It can be seen that for the unwatermarked images, the maximum and the mean watermark similarities are all around 0.5. This is because that for the unwatermarked image, the NSCT coefficients are not modified so that the probability that the watermark can be successfully detected is equal to the probability of failed watermark detection. By comparison, the similarities are mostly higher than 0.6 for the proposed method. Similar to the first method, the mean watermark similarities of the watermarked images are higher than the maximum similarities of the unwatermarked images. In this paper, the watermark detection threshold is set to 0.6 experimentally. In other words, if the maximum watermark similarity is bigger than 0.6, the watermark is successfully detected. Except for the numerical value, we can also judge the presence of the watermark by the extracted watermark image. Fig. 17 shows some distorted images and the corresponding extracted watermark images. It can be seen from Fig. 17 that the extracted watermark images are all recognizable, which also demonstrates that the watermarks are successfully detected. In order to further demonstrate the effectiveness of the proposed scheme, we also compare our simulation results with those of another feature point based high capacity watermarking scheme proposed in the literature [13]. In [13], the watermark is a binary image that follows the Gaussian distribution and it is embedded in spatial domain. The simulation results are given in Table 3, in which Smax denotes the maximum watermark similarity and Smean denotes the mean watermark similarity. It can be seen from the table that our proposed scheme can detect the watermark more accurately than the literature [13]. In all cases, the maximum watermark similarities are higher than those of the literature [13]. Except for the general geometric attacks, the proposed scheme is also robust to image shearing and row/column removal. Compared to the literature [13], our scheme embeds the watermark in transform domain so that watermark robustness is better. 5. Conclusions This paper presents two novel robust image watermarking schemes in order to resist both traditional signal processing attacks and geometric attacks. The proposed schemes are based on watermark synchronization using scale-space feature point based local characteristic regions. Our contributing work is focused on the rotation invariant watermark embedding based on the sale-adapted characteristic regions. The proposed schemes make good use of the rotation invariant angle to

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design the content based watermark embedding. The proposed two schemes embed a binary watermark sequence and a binary watermark image to the cover signal, respectively. Especially for the high capacity watermark embedding scheme, the embedded watermark can be as large as the local region. We have done extensive simulation results to demonstrate the advantages of the proposed schemes. Comparisons also show that the proposed schemes outperform the existing schemes. Acknowledgements This work is supported by National Natural Science Foundation of China under Grant No. 60802077 and Scientific Research Foundation of China University of Mining and Technology (CUMT). The authors thank the anonymous reviewers for their valuable comments, based on which this paper is improved a lot. References [1] M. Alghoniemy, A.H. Tewfik, Geometric invariance in image watermarking, IEEE Transactions on Image Processing 13 (2) (2004) 145–153. [2] P. Bas, J. Chassery, B. 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