A robust zero-watermarking scheme using Canny ... - Semantic Scholar

Int. J. Electronic Security and Digital Forensics, Vol. 5, No. 1, 2013

A robust zero-watermarking scheme using Canny edge detector Mahsa Shakeri* and Mansour Jamzad Department of Computer Engineering, Sharif University of Technology, Azadi Avenue, Tehran, Iran E-mail: [email protected] E-mail: [email protected] *Corresponding author Abstract: By expansion of digital multimedia and networking technology, the problem of ownership protection has become increasingly important. Digital watermarking is an efficient way for copyright protection of digital images. Traditional watermarking techniques degrade the quality of host image by embedding a watermark logo. Facing this problem, a new watermarking approach called zero-watermarking has been proposed. In zero-watermarking methods, the watermark does not require to be embedded into the protected image but it uses both the watermark and the protected image to generate a verification map which is registered to a trusted authority for further protection. In this paper we propose a robust zero-watermarking method which uses Canny edge detection and morphological dilation. Experimental results demonstrate that our proposed scheme is robust against common geometric and non-geometric attacks including blurring, JPEG compression, noise addition, sharpening, scaling, rotation, and cropping. In addition, our experimental results show that our method could outperform the most recent related methods in most cases. Keywords: zero-watermarking; copyright protection; trusted authority; verification map; Canny; dilation. Reference to this paper should be made as follows: Shakeri, M. and Jamzad, M. (2013) ‘A robust zero-watermarking scheme using Canny edge detector’, Int. J. Electronic Security and Digital Forensics, Vol. 5, No. 1, pp.25–44. Biographical notes: Mahsa Shakeri received her BS degree from University of Mazandaran, Iran, in 2008 and MS degree from Sharif University of Technology, Tehran, Iran in 2011, both in Computer Engineering. Her research interests include image processing, medical imaging, and computer graphics. Mansour Jamzad received his BS in Applied Math and Operation Research from School of Planning and Computer Application, Tehran, Iran, and MS in Computer Science from McGill University, School of Computer Science, Montreal, Quebec, Canada. He received his PhD in Electrical (Computer) Engineering from Waseda University, Department of Electronics and Communication, Tokyo, Japan. He is an Associate Professor in the Department of Computer Engineering, Sharif University of Technology, Tehran, Iran. His research interests include image processing, machine vision, robotics, AI, computer graphics, and data structures.

Copyright © 2013 Inderscience Enterprises Ltd.

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1

M. Shakeri and M. Jamzad

Introduction

The advent of internet and advancement of computer technologies have enabled convenient and fast exchange of multimedia so the illegal reproduction and modification of digital media has become increasingly serious. Hence, how to protect the intellectual property rights of digital multimedia is an imperative issue. Digital watermarking is one of the solutions to prevent unauthorised use of images. Traditional digital watermarking techniques embed a watermark such as logo, trademark, or copyright information into a host image so that it is not perceptible. The verifier can extract the watermark in order to verify the ownership. These methods have been proposed in two categories: spatial domain methods (Schyndel et al., 1994; Yu et al., 2001; Kimpan et al., 2004; Mahmood and Selin, 2006), which directly modify the intensity value of the image pixels, and frequency domain methods (Chen et al., 2000; Zhang et al., 2004; Shieh et al., 2004; Khelifi et al., 2005; Liu et al., 2006; Cox et al., 2008; Zhu et al., 2010; Kiani and Moghaddam, 2011), which change the frequency coefficients. All of these watermarking techniques, no matter in spatial or frequency domain, modify the host image’s data to embed the watermark. These techniques, depending on the amount of embedded data, will distort the content of host image which results in quality degradation of watermarked image. Therefore, a new watermarking approach called zero-watermarking has been proposed in which the essential characters of the host image are used for copyright protection, while the content of host image is not modified; as a result, the quality of host image remains intact. Recently, a group of zero-watermarking methods have been proposed that extract some binary features as binary map from the host image. Then by applying XOR between the binary map and a binary watermark logo, a verification map is achieved. This verification map is registered in a trusted authority (TA) for ownership protection. Also, digital signature and timestamp is used in these groups of zero-watermarking algorithms to make the watermark system safer. In watermark verification step, the ownership of a test image, which can be either the zero-watermarked image or an attacked version of it, is verified. A verifier can use the public keys to verify the timestamp and digital signature. Then, the verification key is XORed with the binary map (which is extracted from the test image) to retrieve the watermark logo. Attacks in traditional watermarking methods try to remove the watermark from a watermarked image as much as possible. But in zero-watermarking methods, attacks aim is to degrade the image pixels such that a verification map extracted from the distorted image becomes different from that extracted from non-attacked version of the image. In both approaches the attacks are aimed to disturb the process of ownership proving. In recent years, several watermarking methods have been proposed in zero-watermarking group. Chen et al. (2005) proposed a copyright proving scheme based on t-level wavelet transform in which the features of host image were extracted from t-level LL subband. This scheme is not robust enough under some image processing attacks. Chang and Lin (2008) proposed a new zero-watermarking method that extracts features by using Sobel operators (Kazakova et al., 2004). This method allows the owner to adjust the strength of watermarks through a threshold. But as features in this method are too dependent on the chosen threshold, a wrong threshold can increase false positive significantly. A robust DWT-based copyright proving scheme with fuzzy adaptive resonance theory (ART) was proposed by Chang et al. (2009). This scheme, which

A robust zero-watermarking scheme using Canny edge detector

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combines DWT, fuzzy ART, and quantisation process, converts an image into a short robust table. The low frequency subband LLt is obtained using t-level wavelet transformation. Then the fuzzy ART clustering in which the number of clusters can be adaptively determined is applied on LLt. The binary map is constructed using the concept of quantisation process. Lin et al. (2009) proposed an efficient robust method using 1/T rate forward error correction (FEC). In this method, the watermark logo is fused with noise bits to improve the security, and later XORed with the binary map of image by 1/T rate FEC. For generating a binary map, the average grey level of the host image is used as a threshold and each pixel of binary map is set to 1 if the corresponding pixel in the host image is higher than this threshold; otherwise it is set to 0. In logo verification procedure, the watermark bits can be extracted by the majority voting method. This watermarking scheme has been shown to be robust against common attacks, but it still has not high robustness under some geometric attacks such as rotation and cropping. Although some other schemes have been proposed after Lin et al.’s method such as Abdel-Wahab et al. (2009), Zong et al. (2009), Lee et al. (2009, 2010), Lee and Chen (2010), Lizong et al. (2010a, 2010b) and Li et al. (2010), but we do not survey them in this paper because they have lower robustness against common attacks in comparison with Lin et al.’s scheme. In this paper, we propose a copyright protection scheme which first applies Canny edge detector followed by applying the dilating operator on the binary edge image. Then, the watermark logo is tiled T times until it becomes the same size as the binary map. A verification map is computed by applying XOR on the binary map and the tiled logo. In verification procedure, the watermark logo is extracted by using the majority voting method. Experimental results show that our proposed method is efficiently robust against common geometric and non-geometric attacks. Although our method is in spatial domain, it completely achieves the essential properties of copyright protection listed as follows (Chang and Lin, 2008): •

Robustness: The method must be robust against both geometric and non-geometric attacks such as blurring, cropping, JPEG compression, rotation and scaling may occur so that the ownership of the host image can be verified.

•

Unambiguity: The extracted watermark logo must be clear enough to indicate the ownership of host image.

•

Security: Even if intruders know the embedding algorithm, they still cannot extract the embedded watermark without having the secret key.

•

Transparency: The embedded watermark must be perceptually invisible. That is, the embedding process should not distort the image to a noticeable level by the human vision system.

•

Multiple watermarking: The watermarking algorithm must allow image owners to embed multiple watermarks in an image without any interference.

•

Time consumption: Watermark signing and logo verification procedure must be completed in a reasonable time.

•

Blindness: In watermark verification procedure, the original image must not be needed.

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The rest of paper is organised as follows. Our proposed scheme for image ownership protection is elaborated in Section 2, followed by the experimental results and discussion in Section 3. Conclusions are provided in Section 4.

2

The proposed method

By using Canny edge detection operator and morphological dilation, a binary map is extracted from the host image. The watermark logo is tilled until its size becomes the same as the extracted binary map. The binary map and tilled logo are combined to generate a verification key which will be used to register to a trusted third party with digital signature and timestamp. Detailed description of certificate generation algorithm and verification algorithm is stated in Sections 2.1 and 2.2.

2.1 Certificate generation algorithm Let the original image O be a grey-level image of size Ho × Wo and the digital logo W be a binary image of size Hw × Ww. The original image O and the binary logo W are defined as follows: O = {oij 0 ≤ oij ≤ 255, 0 ≤ i ≤ H o , 0 ≤ j ≤ Wo } .

(1)

W = {wij wij ∈ {0, 1}, 0 ≤ 1 ≤ H w , 0 ≤ j ≤ Ww } .

(2)

Step 1

Applying Canny edge operator: Canny edge detector operator CannyEdge is applied on the image O to extract binary features Eo. Eo = CannyEdge ( O, [TL , TH ] , σ ) .

(3)

where TL and TH are the low and high thresholds, respectively. σ is the standard deviation in CannyEdge operator. The resulting Eo is so dependent on choosing the appropriate value for these parameters. In Section 3.1, we discuss about these parameters precisely. Step 2

Applying morphological dilation: Since the edges obtained in previous step are too thin, a dilation operation with radius disk size of 3 as structuring element is applied on the resulting Eo. Eod is obtained as follows: Eod = Dilation ( Eo , disk , 3) .

Step 3

(4)

Logo permutation: To resist geometric distortions, a fast two dimensional pseudorandom permutation with seed s (Hsu and Wu, 1999) is used to permute the digital logo to disperse its spatial relationship. The permuted logo is denoted by Wp.

Wp = {wpij wpij = PRPs ( wij ) , 0 ≤ i, i ≤ H w , 0 ≤ j , j ≤ Ww } where PRPs is the permuted function with seed s.

(5)

A robust zero-watermarking scheme using Canny edge detector Step 4

Step 5

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Tiling the watermark logo: To improve the robustness, the permuted logo is tiled T times repeatedly until the size of the new Wptiled becomes the same size as Eod. T is calculated according to the logo size and image size as equation (6). ⎢ H ⎥ ⎢W ⎥ T = ⎢ o ⎥×⎢ o ⎥ ⎣ H w ⎦ ⎣Ww ⎦

(6)

Wptiled = {wptiledij wptiledij ∈ {0, 1}, 0 ≤ i ≤ H o , 0 ≤ j ≤ Wo } .

(7)

Generate the verification key: The verification key T is generated by applying XOR operation on the binary map Eod and the tiled logo Wptiled. Note that the size of K is still Ho × Wo. K = Eod ⊕ Wptiled

Step 6

(8)

Compression of verification key: A compression function is used to reduce the size of verification key K. Since K consists of zero and one, each pixel of K can be reduced from one byte to one bit. Therefore, K can be effectively compressed. For example, if K is of size 512 × 512, it can be decreased to 32 k-byte by using this compression. Ck = compression( K ).

(9)

where Ck is the resulting compressed verification key. Figure 1

The certificate generation procedure (see online version for colours) Choosing σ

Canny feature extraction

Original Image 1

2

CannyEdge operator

Watermark Logo

Permuted Logo

Dialation

1/T rate FEC

3

+

4

5 Verification Key

Compressed Verification Key 7 Trusted Authority

6

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Step 7

Digital signature and timestamping: The compressed verification key Ck, the width and height of original image, the seed s, and the signer’s identity IDsigner is sent to a trusted authority. The trusted authority centre generates the certificate CTA for the host image as follows: CTA = HashTA ( Ck || WO || H O || IDsigner || s || ts ) .

(10)

where HashTA(.) is a one-way hash function, ts is a timestamp and | | is the concatenation operator. At last, the trusted authority centre publishes the certificate CTA, timestamp ts and the hash function HashTA(.) on its bulletin board. If there is any dispute the certificate CTA and the timestamp ts can be used to verify the ownership of host image O. Figure 1 shows the certificate generation process.

2.2 Logo verification procedure In verification procedure the logo is retrieved without using original image. Steps of this procedure are as follows: Step 1

Getting the information from the trusted authority: First, obtain the verifier receives CTA, timestamp ts and hash function HashTA(.) from trusted authority. * using {Ck, WO, HO, IDsigner, s} and ts as follows: Then compute CTA * CTA = HashTA ( Ck || WO || H O || IDsigner || s || ts ) .

(11)

* is not the same as CTA, the verification procedure is If the resulted CTA terminated in this phase and the validity of information is not approved; otherwise, the validity of information is verified and the verification procedure is continued in following steps:

Step 2

Decompressing Ck: The Ck is decompressed by Decompress function to obtain the verification key K. K = Decompress (Ck ).

Step 3

(12)

Applying Canny edge operator: CannyEdge operator is used to extract binary features Eo′ from the image O′. Eo′ = CannyEdge ( O ′, [TL′ , TH′ ] , σ ′ ) .

(13)

where TL′ and TH′ are the low and high thresholds, respectively. σ′ is the standard deviation in the CannyEdge operator. Step 4

Applying morphological dilation: A dilation operation with radius disk of size 3, as structuring element is used to thicken the edge width of the binary feature map Eo′ . ′ = Dilation ( Eo′ , disk , 3) . Eod ′ is the dilated version of Eo′ . where Eod

(14)

A robust zero-watermarking scheme using Canny edge detector Step 5

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′ : The XOR operation between Eod ′ and K is used to extract Extracting Wptiled ′ . Wptiled ′ = Eod ′ ⊕ K. Wptiled

(15)

where ′ = {wptiled ′ ij wptiled ′ ij ∈ {0, 1}, 0 ≤ i ≤ H o , 0 ≤ j ≤ Wo } . Wptiled

Step 6

(16)

′ : Dividing Wptiled ′ into T blocks called Blockt. The value of T is Dividing Wptiled the same as that defined in equation (6). ⎪⎧blx, y blx , y ∈ {0, 1}, H w × (t − 1) ≤ x ≤ H w × t − 1, ⎪⎫ Blockt = ⎨ ⎬ ⎪⎩Ww × (t − 1) ≤ y ≤ Ww × t − 1, 1 ≤ t ≤ T ⎪⎭

Step 7

(17)

Majority voting to obtain the permuted logo Wp′: By using majority voting on T blocks Blockt, we can determine if each bit of Wp′ is zero or one.

Wp ′ = MajorityVote ( Blockt ) , 1 ≤ t ≤ T

(18)

where Wp ′ = {wpij′ | wpij′ ∈ {0, 1}, 0 ≤ i ≤ H w , 0 ≤ j ≤ Ww }. The MajorityVote function calculates the maximum number of zero and one bits in the same position of each Blockt, 1 ≤ t ≤ T. Wp′ is the permuted logo. Extracting the main logo is done in the next step. Figure 2

The logo verification procedure (see online version for colours) Choosing σ

Canny feature extraction

Original Image 3

4

CannyEdge operator

Extracted Logo 8

Dialation

1/T rate FEC

Permuted Logo

5

+

6 7

Verification Key

Decompressed Verification Key 1 Trusted Authority

2

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Step 8

Retrieving the watermark logo: The inverse pseudorandom permutation of the permuted logo Wp′ is computed by function PRPs−1 and seed s. The retrieved logo W′ is as follows: W ′ = {wij′ wij′ = PRPs−1 ( wpij′ ) , 0 ≤ i, i ≤ H w , 0 ≤ j , j ≤ Ww } .

(19)

Finally, the verifier can visually verify the accuracy of retrieved logo and validate the ownership of the test image. Figure 2 shows the logo verification procedure in detail.

3

Experimental results and discussion

3.1 Experimental results We evaluate the quality between the original image O and the attacked image O′ by using the peak signal to noise ratio (PSNR) which is defined as follows: max ( Oij ) × WO × H O 2

PSNR = 10 log10

2 ∑ (Oij − Oij′ )

.

(20)

where Oij and Oij′ are image pixel values at coordinate (i, j). WO is the width and HO is the height of image O, respectively. There are two measures to estimate the correctness of an extracted watermark: normalised cross correlation (NC) and accuracy rate (AR). NC for a binary watermark is defined as follows:

∑ ∑ w × w′ NC = (w, w′) = ∑ ∑ w Hw

Ww

Hw

ij j =1 Ww

i =1

j =1

i =1

=

ij

2 ij

(21)

Number of correctly retrieved pixels − Number of incorrectly retrieved pixels Total number of watermaks pixels

where wij and wij′ are the values that are located in coordinate (i, j) of the original watermark w and the extracted watermark logo w′. Here, wij is set to –1 if it is equal to 0. Otherwise, it is set to 1. The bits of wij′ is set in the same way. Therefore, NC ∈ [–1, 1]. Since the bits are set to –1 and 1, the NC formula is equal to number of correctly retrieved pixels minus number of incorrectly retrieved pixels divided by total number of watermark pixels. Some papers have used the AR measure as defined below, to evaluate the robustness of their methods.

∑ ∑ AR = Hw

Ww

i =1

j =1

wij ⊕ wij′

H w × Ww

=

Number of correctly retrieved pixels Total number of watermark pixels

(22)


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where wij and wij′ are the original and the retrieved watermark logo, Ww and Hw are the width and height of the watermark logo, respectively. Since the pixels of W and W′ remain 0 and 1, the AR formula is equal to number of correctly retrieved pixels divided by total number of pixels. Although these two measures are different, they can convert to each other easily. As the number of incorrect pixels can be evaluated by subtracting the number of correct pixels from total number of pixels, the NC value can be obtained from AR. CannyEdge operator has three parameters that need to be established: two thresholds and the standard deviation of input image (σ). The appropriate values for thresholds reduce false positive edge points in the resulting edge map. The high threshold is set to 0.7 of the total number of pixels and the lower threshold according to Canny’s (1986) suggestion is set to one third of the high threshold. Figure 3

Effect of choosing inappropriate value of σ in Canny operator on the resulting edge map, (a) The original image (b) The edge map by choosing a too high σ (c) The binary feature map after applying dilation on (b) (d) The edge map by choosing a too low σ (e) The binary feature map after applying dilation on (d)

(a)

(b)

(d)

(c)

(e)

Varying the value of σ produces different edge maps. If σ is too large, few information will be generated and if it is too small, too many detailed information will be retained. If σ is selected inappropriately, after applying the dilation operation on the resulting edge map we will have a messy incorrect feature map. Figure 3 illustrates the effect of choosing an inappropriate value of σ. As shown in Figure 3(b), by using a too high value for σ too many information is lost. Figure 3(c) shows the binary maps after applying dilation. Figure 3(d) illustrates the result of using σ with a too low value. In this case, too

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many detailed information is preserved. As shown in Figure 3(e), after applying dilation on Figure 3(d) we will not have a distinct binary feature map. Incorrect and indistinct binary maps causes to have a high false positive in our approach, therefore we need to choose an appropriate value for σ. False positive in zero-watermarking means that a watermark logo is extracted from an unwatermarked image. For example, if our original image is ‘Lena’ we should not be able to extract our binary logo from ‘Baboon’ image. In other words, if the watermark is extracted from Baboon or an image different from Lena, we have a false positive. In the following, we discuss how to reduce the false positive by selecting an appropriate value for σ. As the value of σ for an image depends on the complexity of image, we classified images of Washington University image database into low, medium and high complexity based on quad-tree representation according to equation (23) suggested by Yaghmaee and Jamzad (2005). The quad-tree complexity measure for an image I is as follows: Cm( I ) =

nB − 1 nP

(23)

where for image I, Cm(I) is the complexity measure, nB is the number of blocks in quad-tree decomposition and nP is the total number of pixels. Higher values for Cm represents images with higher complexity. After classifying images of Washington University database into low, medium and high complexity, we randomly selected four images from each complexity as test images. The rows in Table 1 show four images with the same complexity. By the following experiments, we choose the best σ for each complexity in order to reduce the false positive error. We select one of the images of the first row of Table 1 as the original image and one of the other images in the same row as test images. For example according to the second column of Table 2, we choose image (a) as the original image and pass it to the certificate generation algorithm. Then we select the test image (b) and give it to the verification algorithm. We run our method by varying σ from 0.2 to 5 (with 0.2 increment step value), and compute the NC for each σ. For other pairs of original and test images that are determined in columns of Table 2 we do the same. The average NC among 12 pairs of original and test images in columns of Table 2 is calculated over each σ. Figure 4 shows the average NC as a function of various σs. According to Figure 4, average NC has the lowest value for σ = 1.4. Therefore, for low complex images if σ is set to 1.4, the false positive will be minimum. For medium complex images that are shown in second row of Table 1, we do the same experiment as above. Each time we choose one of the four images as original and give it to the certificate generation algorithm. Also we select another image as test image and give it to verification algorithm. We run our method by varying σ from 0.2 to 5 (with 0.2 increment step value) and compute NC for each σ. The columns of Table 3 show the different 12 choices of original and test images. Finally, the average NC among these 12 pairs of original and test images is calculated. Figure 5 shows the average NC over various σ. According to Figure 5, the best value of σ for medium complex images that has the lowest false positive is 2.

A robust zero-watermarking scheme using Canny edge detector Table 1

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The four randomly selected images from each complexity

Low complexity

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

(j)

(k)

(l)

Medium complexity

High complexity

Table 2

Twelve pairs of original and test images from low complex images in Table 1

Original image

(a)

(a)

(a)

(b)

(b)

(b)

(c)

(c)

(c)

(d)

(d)

(d)

Test image

(b)

(c)

(d)

(a)

(c)

(d)

(a)

(b)

(d)

(a)

(b)

(c)

Figure 4

The average NC value over each σ for 12 low complex pairs of images of Table 2 (see online version for colours)

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Table 3

Twelve pairs of original and test images from medium complex images in Table 1

Original image

(e)

(e)

(e)

(f)

(f)

(f)

(g)

(g)

(g)

(h)

(h)

(h)

Test Image

(f)

(g)

(h)

(e)

(g)

(h)

(e)

(f)

(h)

(e)

(f)

(g)

Figure 5

The average NC value over each σ for 12 medium complex pairs of images of Table 3 (see online version for colours)

Table 4

Twelve pairs of original and test images from high complex images in Table 1

Original image

(i)

(i)

(i)

(j)

(j)

(j)

(k)

(k)

(k)

(l)

(l)

(l)

Test image

(j)

(k)

(l)

(i)

(k)

(l)

(i)

(j)

(l)

(i)

(j)

(k)

Figure 6

The average NC value over each σ for 12 high complex pairs of images of Table 4 (see online version for colours)


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The value of σ for each complexity

Image complexity The value of σ

Low complex image

Medium complex image

High complex image

1.4

2

2.2

The images of the third row of Table 1 are used to determine the appropriate value of σ for high complex images. We do the same experiment as above by using 12 choices of Table 4 as original and test images. Figure 6 shows the average NC as a function of σ; Also it indicates that the best value of σ that has the lowest false positive for high complex images is 2.2. Therefore, to obtain a high retrieval rate, parameter σ should be appropriately selected according to the complexity of image. Before starting the certificate generation algorithm and the verification algorithm we compute the complexity of the input image by using equation (23). Then the value of σ is selected according to Table 5. We use the same test image and binary logo as Lin et al. (2009) in our experiments for better comparison. Lena image is of size 512 × 512 and the binary watermark logo is of size 64 × 64 as shown in Figure 7. Figure 7

(a) Test image Lena (b) Binary watermark logo

(a)

(b)

An attack in zero-watermarking methods is defined as any kind of distortions that can be applied on the original image in order to disturb the procedure of binary feature extraction in watermark verification phase. We consider both geometric and non-geometric attacks including blurring, JPEG compression, noise addition, sharpening, scaling, rotation, cropping, scaling-cropping, and blind pattern matching (BPM) as defined in bellow. •

Rotation: Lena is rotated clockwise at 20° and then resized to 512 × 512.

•

Quarter cropping: the left-top corner of the image is cropped.

•

Surround cropping: 100 pixels are cropped from Lena on the surrounding of each side.

•

Scaling: Lena is first scaled to 16 × 16, then the scaled image is resized to 512 × 512.

•

Sharpening: the image is sharpened such that the PSNR value is decreased to 14 dB.

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•

Histogram equalisation: applying this routine on Lena reduces its PSNR to 17 dB.

•

JPEG: JPEG compression with quality factor 1 is applied on the image.

•

Noise: Gaussian noise is added to Lena until PSNR value for the attacked image is reduced to 9 dB.

•

Blurring: Lena is blurred such that the PSNR value of the attacked image is decreased to 23 dB.

•

BPM: The Peppers image is encoded using LBG algorithm (Linde et al., 1980) to obtain a codebook with a size of 256 ×16. Then, Lena is divided into 4 × 4 non-overlapping blocks. Then we replaced each block in Lena with the closest codeword in Peppers to form perceptually similar images.

The resulted images under above attacks, corresponding PSNR values, extracted binary watermark, and corresponding NC for Lena image are shown in Tables 6–8. Experimental results demonstrate that in worst cases the proposed method can still retrieve a clear and recognisable binary watermark. We also combined various attacks to test our proposed method such as scale + cropping (the test image is scaled from 512 × 512 to 670 × 670 and then we cut the border area of test image to form a 512 × 512 image). Scale + JPEG (the test image is scaled to 64 × 64 then resized to 512 × 512 after that a JPEG compression with quality factor 1 was applied on it). Cropping + blurring (80 pixels were cropped from Lena on its surrounding area, then it was blurred until its PSNR value reduced to 7 dB). Rotation + noise (the test image was rotated 2.5° and then Gaussian noise was added to the test image such that its PSNR value reduced to 14 dB). And blurring + noise (blurring and adding noise were applied on the test image to decrease its PSNR to 13 dB). After these attacks, as shown in Tables 9 and 10, the retrieved watermark can still be recognised clearly. Table 6

Normalised correlation coefficients (NC) under rotation, quarter cropping, and surround cropping attacks Rotation

Quarter cropping

Surround cropping

10

10

6

0.99

1

1

Attacked image

PSNR Retrieved logo

NC


39

Normalised correlation coefficients (NC) under sharpening, histogram equalisation, and scaling attacks Sharpening

Histogram equalisation

Scaling

14

17

19

1

1

0.99

Attacked image

PSNR Retrieved logo

NC Table 8

Normalised correlation coefficients (NC) under JPEG, adding noise, and blurring attacks JPEG

Adding noise

Blurring

22

12

20

1

0.85

0.92

Attacked image

PSNR Retrieved logo

NC

In Table 11, we compare our proposed method to Chen et al. (2005), Chang and Lin (2008), Chang et al. (2009) and Lin et al. (2009). Despite of Lin et al.’s method which compared the reported AR values of previous schemes with NC values of their method, we first convert the reported AR values of other schemes to NC, as described in Section 3.1, and then make a comparison with our proposed method (Table 11). Lena of size 512 × 512 and a 64 × 64 binary watermark are used in the experiments. Lena in each case is attacked by either more severe attacks or the same attacks than previous methods.

40 Table 9

M. Shakeri and M. Jamzad Normalised correlation coefficients (NC) under BPM, scaling + cropping, and scaling + JPEG attacks BPM

Scaling + cropping

Scaling + JPEG

29

13

19

1

0.96

0.99

Attacked image

PSNR Retrieved logo

NC Table 10

Normalised correlation coefficients (NC) under surround cropping + blurring, rotation + noise, and blurring + noise attacks Surround cropping + blurring

Rotation + noise

Blurring + noise

7

14

13

1

1

0.98

Attacked image

PSNR Retrieved logo

NC

As can be seen from Table 11, although PSNR of our method is less than the previous methods in most cases, but the value of NC under each attack is higher. Except for blurring that Lin et al.’s method has better performance. Notice that the NC value of our proposed method for blurring is 0.99 that is still high and can retrieve almost the complete binary watermark. Also the proposed method has a higher NC value for cropping and rotation than Lin et al.’s method. Table 11 demonstrates that Chen et al.’s and Chang and Lin’s methods failed in rotation and cropping attacks; the Chang et al.’s although has higher NC values in two mentioned attacks, it has lower NC values against non-geometric attacks; the Lin et al.’s method has high performance in comparison with the three mentioned methods but still has lower NC value than our proposed method in average.

29

31

30

28

29

14

11

16

Blurring

JPEG

Noise

Sharpening

Scaling

Rotation

Quarter cropping

Cropping + scaling

PSNR

0.6

0.74

0.64

0.98

0.98

0.98

0.96

0.98

NC

Chen et al.’s method

19

23

14

29

28

23

37

29

PSNR

0.72

0.84

0.4

0.98

0.98

0.96

0.98

0.96

NC

Chang and Lin’s method

15

11

14

29

27

30

30

29

PSNR

0.82

0.92

0.80

0.90

0.92

0.94

0.94

0.90

NC

Chang et al.’s method

15

11

13

20

14

16

22

23

PSNR

1

0.98

0.99

1

1

1

1

1

NC

Lin et al.’s method

15

10

13

19

14

14

22

23

PSNR

1

1

1

1

1

1

1

0.99

NC

Proposed method

Table 11

Attack

A robust zero-watermarking scheme using Canny edge detector Comparison of the proposed method with the related schemes

41

42


3.2 Discussion Our scheme not only inherits the advantages of zero-watermarking schemes but also possesses better robustness than that of other related schemes. Also, our scheme can achieve the requirements of copyright protection mechanism such as: •

robustness: very high NC value (at least 0.99) as shown in Table 1

•

unambiguity: the retrieved logo can clearly be recognised according to the high value of NC

•

security: which is based on the difficulty of breaking the one-way hash function

•

transparency: our scheme is lossless and does not modify the pixels of host image

•

multiple watermarking: the trusted authority can generate a distinct certification for each watermark logo so multiple logo is possible

•

time consumption: the proposed scheme is in spatial domain and requires lower running time than the related methods which are in frequency domain

•

blindness: our proposed method does not need the host image to extract the logo in verification procedure.

The proposed scheme can also be applied to a grey-level watermark logo. We can decompose a grey-level watermark logo into eight binary bitplanes. Each bitplane is regarded as a binary watermark logo so we will have a grey-level verification map with eight bitplanes. In addition, we can apply our method to colour images by dividing an image into RGB and applying our method on these three components separately.

4

Conclusions

Recently, a group of digital watermarking methods called zero-watermarking have been proposed which do not degrade the quality of host image. In these methods, some binary features are extracted from the host image then XORed with a watermark logo. The resulted verification map is sent to a trusted third party for further protection. The important issue in this group of watermarking is how to retrieve significant binary features from the image to be protected in order to achieve an appropriate verification map. In this paper we proposed a blind zero-watermarking scheme in spatial domain that utilises Canny edge detection and morphological dilation for extracting a binary map. The resulted binary map is then XORed with a binary watermark to generate a verification map. In verification process, the watermark logo is extracted by the majority voting method. The proposed method has low computation time in comparison with most of the related schemes because it is implemented in spatial domain and it saves the transform domain computation. Experimental results show that the proposed scheme has high performance against common geometric and non-geometric attacks. Consequently,


43

according to the simulation results our proposed method outperforms other similar schemes in case of most attacks.

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