An improved robust image watermarking method using DCT and YCoCg-R color space

Accepted Manuscript Title: An Improved Robust Image Watermarking Method Using DCT and YCoCg-R Color Space Author: Mohammad Moosazadeh Gholamhossein Ekbatanifard PII: DOI: Reference:

S0030-4026(17)30533-8 http://dx.doi.org/doi:10.1016/j.ijleo.2017.05.011 IJLEO 59156

To appear in: Received date: Accepted date:

15-8-2016 4-5-2017

Please cite this article as: Mohammad Moosazadeh, Gholamhossein Ekbatanifard, An Improved Robust Image Watermarking Method Using DCT and YCoCg-R Color Space, (2017), http://dx.doi.org/10.1016/j.ijleo.2017.05.011 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

paper.tex

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An Improved Robust Image Watermarking Method Using DCT and YCoCg-R Color Space Mohammad Moosazadeha,∗, Gholamhossein Ekbatanifardb,∗ a Department

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of Information Technology Engineering, Mehrastan University, Astaneh Ashrafiyeh, Gilan, Iran b Department of Computer Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Gilan, Iran

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Abstract

Watermarking is one of the solutions to prevent unauthorized use of digital media. This solution can help the protection of ownership rights by embedding

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the copyright information in the intended media. In this paper, a digital image watermarking algorithm in YCoCg-R color space is proposed. Thriplet components of YCoCg-R color space have a good decorrelation and changing one

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component has the least impact on the two other color components. So it can be

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effective in increasing the robustness of watermarking against various attacks. For watermark embedding the proposed method uses Discrete Cosine Transform and its coefficients relationship. During watermark bits embedding, complexity amount of host image blocks is utilized to select the target blocks and energy

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value of host image blocks is used to adaptive selection of embedding strengths that it causes a very high robustness especially against JPEG compression. The Arnold transformation, also, plays an important role in increasing the security of our proposed method by scrambling the watermark. Considering the conflict among three main requirements of watermarking, including imperceptibility, robustness and capacity, the proposed method is compared with other algorithms and the results show the superior robustness of the proposed method over the other algorithms with same capacity. ∗ Corresponding

author Email addresses: [email protected] (Mohammad Moosazadeh), [email protected] (Gholamhossein Ekbatanifard)

Preprint submitted to Journal of LATEX Templates

August 15, 2016

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Keywords: Robust image watermarking, Coefficients relation, Discrete Cosine Transform (DCT), YCoCg-R color space, JPEG compression, Arnold

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Transform Map (ATM)

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2010 MSC: 68U10

1. Introduction

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Nowadays with pervasive growth of Internet and increasing the speed of communication networks, websites and social networks are used in humans real life as some of the basic tools. These applications provide users with easy access to digital assets such as images, videos, sounds, etc. So they can buy and

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sell or copy the digital media without the permission of their owners. There-

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fore, the protection of ownership rights of digital media has become a challenge for content producers. One of the methods of dealing with this issue is digital watermarking. Digital watermarking is embedding a piece of digital copyright information in the media that can be a logo or a pseudo-random sequence. The

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term digital watermarking was first used by Tirkel in 1993 [1]. Digital wa-

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termarking algorithms are generally divided into two domains: spatial domain methods in which digital watermarks are add into image pixel values directly, and frequency domain methods that embed digital watermarks by changing the

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values of host image transforms coefficients. Meanwhile, algorithm plays a vital role in watermarking quality, and if the used watermarking technique is efficient, the embedded watermark cannot easily be detected [1]. Another category for watermarking consists of robust watermarking for protecting the ownership rights of digital media and fragile watermarking for authenticating the digital

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media [2].

Various algorithms have been designed and tested for watermark embedding

so far. In [3], a watermarking algorithm in YCbCr color space is presented by integration spatial and frequency domains. In this method, after 8×8 blocking the Y component of the host image, the DC coefficient of each block is computed 25

directly without DCT. Then in order to watermark embedding, DC coefficient

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value is changed based on the corresponding watermark bit and quantization step and then a percentage of this changing amount is added to the other pixels

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of the block in the spatial domain. In [4] at first, DWT is applied on the host image and then with help of multiple scaling factors, the block wise singular

components are used for watermark embedding. In this method, in order to

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find the values of scaling factors, the ABC (artificial bee colony) is used. In

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[5] four neighbor 8×8 blocks from the transform coefficients of the blue channel of the host image are selected and a low-frequency coefficient is determined by the same position in all four blocks, then one coefficient is selected randomly from these four coefficients for watermark embedding and the others are used as

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references. The watermark image is encoded by BCH code, and then embedded into the selected DCT coefficients. In [6], a robust watermarking method is

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proposed that consists of two embedding stages. In the first stage, the image is divided into several blocks and the Contourlet transform (CT) is performed 40

on each block. Then the watermark is embedded in the high frequency com-

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ponent of each block. In the second stage, the Contourlet transform (CT) is

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applied on global image and the watermark is embedded in the low frequency component. During watermark extracting, with the help of a measure, between the two extracted watermarks, the most appropriate watermark is selected. The

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reasoning of this method is that by embedding the watermark in the local as well as the global CT coefficients of two different frequency bands, it could obtained good robustness against various attacks. In [7], a ridgelet-based watermarking algorithm is proposed which modulates ridgelet coefficients on strongest energy direction using an energy modulation technique, and then by designing a wa-

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termarking framework based on tissue P systems, a special membrane structure is designed and its cells are used as parallel computing units to find the optimal watermarking parameters. Digital watermarking in [8] by changing the angle quantization index modulation (AQIM) method, proposes a new method, called the difference AQIM (DAQIM) method that embeds the watermark bit

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by quantizing the difference of the two angles formed by two vectors instead of quantizing the angles directly. In [9], a method called the improved AQIM 3

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(IAQIM) is proposed which has also tried to improve the AQIM, by two ways: designing the minimum distortion angle quantization and using the amplitude

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projection strategy.

Our goal in this paper is to propose a method of robust watermarking against

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various attacks especially JPEG compression. We have considered five principles to acquire a robust method:

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• transferring the host image to a suitable color space with the lowest correlation between its components

• considering a good feature to select the target blocks for watermark em-

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bedding

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• choosing the optimal embedding strengths for each block separately • applying a suitable embedding method stable against changes • increasing the watermark security by an appropriate reversible scrambling method.

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In this paper, by considering these five principles, we have introduced a new robust watermarking algorithm that uses YCoCg-R color space. For choosing the target blocks for watermark embedding and choosing the optimal embedding

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strengths, the complexity amount of the blocks and the mean of its coefficients

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are considered respectively. Also for watermark bits embedding, the relation between DCT coefficients and for scrambling the watermark, the Arnold transformation are used.

The remaining part of this paper is organized as follows: In section 2, 3 and

4 Discrete Cosine Transform, Arnold transform map and YCoCg-R color space

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are described respectively. In section 5 the proposed watermarking method is explained. Performance evaluation and experimental discussion of the proposed method is presented in section 6 and performance comparison with other algorithms is given in section 7. Finally, the conclusion of this paper is drawn in section 8.

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Table 1: DCT coefficients order in zigzag scan

2. Discrete Cosine Transform

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One of the methods for transforming spatial domain to frequency domain is Discrete Cosine Transform or DCT. According to Table1, by using zigzag scan, the obtained coefficients can be divided into three different frequency

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bands, including high, middle and low frequency bands. The low frequency band coefficients have most energy and in fact, they are perceptually significant

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portion of image. Also the high frequency band coefficients have lowest energy and they are very vulnerable against attacks. So the watermark is usually embedded into middle frequency band (including 7 to 28 coefficients) ) [10].

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DCT-based watermarking algorithms are more robust than spatial domain

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methods. They have robustness against common image processing operations such as low pass filtering, brightness and contrast adjustment, blurring etc. But, they are weak against geometric attacks such as rotation, scaling, cropping etc. DCT watermarking field is classified in two categories including global DCT watermarking and block-based DCT watermarking [1].

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3. Arnold Transform Map To scramble the images, lots of methods such as Arnold Transformation, Magic Transformation, Hilbert Curve, Conway Game, Broad Gray Code Transformation and Orthogonal Latin Square Transformation are used [11]. Our 5

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that is defined as Eq.(1) [12]:    x0 1  = y0 1

1 2

 

x y

  (modN )

(1)

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choice to scramble the watermark image is Arnold Transform Map or ATM

In which (x, y) are the original image pixels, and (x’, y’) are the scrambled image pixels, and N is order watermark image. Arnold transforming as a reversible

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transformation has a specific feature: after certain times of this transforming, image returns to its original state. Therefore, watermarking image scrambling time can be used as watermarking key.

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4. YCoCg-R Color Space

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Most of the watermarking schemes are presented for gray scale images. Some watermarking methods are, also, implemented on color images that are mainly performed on RGB, YUV, YIQ and YCbCr color spaces [3, 5, 12, 13]. Although the algorithms presented for gray images can be generalized to work

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with color images, but most of the times the ultimate action is not satisfactory [14]. So choosing color space for watermark embedding is an important issue for researchers, investigated in [15, 16].

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For achieving the robustness in watermarking, a change in one color compo-

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nent from the host image should not impact on other color components. Color spaces like YCbCr have good decorrelation, but even better decorrelation can be obtained by the YCoCg [17]. The YCoCg color space breaks down a color image into three components Luminance (Y), Chrominance orange (Co) and Chrominance green (Cg). Compared with RGB, this color space, provides a

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good decorrelation [18].

YCoCg-R is the reversible integer to integer version of YCoCg. According to Eq.(2), the RGB to YCoCg-R transformation and its inverse are defined as

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[19]: t = Y + bCg/2c

t = B + bCo/2c

⇐⇒

G = Cg + t B = t − bCo/2c

Y = t + bCg/2c

R = B + Co

(2)

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Cg = G − t

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Co = R − B

YCoCg-R features are similar to YCoCg, but it provides better reversibility. For lossless encoding, using a reversible transformation can be useful and

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YCoCg-R has this ability. In [17] the coding gain YCoCg-R is compared to several YCbCr transforms specified in H.264/AVC Annex E, and the YCoCg-R

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has a 0.7 dB improvement. Also the YCoCg-R color space can improve PSNR as much as 3 dB.

5. Proposed Method

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The proposed method utilizes the relationship between one selected middle

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frequency coefficient and five low frequency coefficients from each block to embed the watermark bits. Based on our tests, we have selected YCoCg-R color space

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for watermarking due to low dependence of its color components to each other. Also in order to increase the security, we have used Arnold transform map for scrambling the watermark. In embedding procedure, we have considered block

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complexity feature for selecting the suitable blocks and also we have chosen embedding strength for each block adaptively based on energy value of each block. This section will describe the proposed method in detail and the block

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diagram of the proposed watermark embedding and extraction algorithm is described in Fig.1 and Fig.2 respectively. 5.1. Watermark Embedding Algorithm The proposed model for watermark embedding includes 10 steps as follows: Step 1. Scrambling watermark image by Arnold transformation (5 times)

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Step 2. Converting the host image from RGB to YCoCg-R color space and separating the three components Y, Co and Cg.

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Step 3. Selecting the Y component for watermark embedding, and transferring

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it to frequency domain by DCT and performing 8×8 blocking. Step 4. Calculating the complexity of each block by variance function, then 155

sorting and selecting the most complex blocks for watermark embedding

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in order to increasing the resistance of watermarking against JPEG compression.

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Step 5. Calculating the energy amount of each block by mean function and selecting embedding strength (α) based on energy value of each block 160

according to Eq.(3), So that for a block with lower energy, larger α

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value is selected.

α = F 1 × (1 − M )

(3)

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Where M means the result of applying mean function on each block and F1 is the impact factor of mean value to create a balance in robustness and imperceptibility.

Step 6. Running the zigzag scan on each block and dividing its coefficients into

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low, middle and high bands according to Table1.

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Step 7. Performing embedding operations in each block according to Eq.(4) R = (C(2) + C(3) + C(4) + C(5) + C(6) ) × F 2   R − α if R − C < α and W = 0  i (x)   0 C(x) = R + α if C(x) − R < α and Wi = 1     C

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(4)

(x)

In which C(2) , C(3) , C(4) , C(5) and C(6) are the low frequency coefficients

in position 2 to 6 in zigzag scan, F2 is the impact factor of low fre-

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quency coefficients, C(x) is the selected coefficient for embedding, α is 0 the embedding strength, Wi is corresponding watermark bit and C(x)

is watermarked coefficient in selected block. Step 8. Repeating the steps 5 to 7 for embedding all watermark bits. Step 9. Returning watermarked Y component from frequency domain to spatial 175

domain by inverse DCT.

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Figure 1: Watermark embedding model

Step 10. Combining watermarked Y component with two other original compo-

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nents and then returning host image from YCoCg-R to RGB color space and finally obtaining the watermarked image.

5.2. Watermark Extraction Algorithm

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The proposed model for watermark extraction includes 6 steps as follows:

Step 1. Converting the watermarked image from RGB to YCoCg-R color space and separating the three components Y, Co and Cg. Step 2. Selecting the Y component for watermark extraction, and transferring it to frequency domain by DCT and perform 8×8 blocking.

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Step 3. Selecting watermarked blocks based on the embedding sorting.

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Figure 2: Watermark extracting model

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Step 4. Running the zigzag scan on each block and dividing its coefficients into low, middle and high bands according to Table1. Step 5. Performing extracting operations according to Eq.(5)

(5)

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0 0 0 0 0 R0 = (C(2) + C(3) + C(4) + C(5) + C(6) ) × F2   1 if C 0 > R0 (x) Wi0 =  0 otherwise

0 0 0 0 0 In which C(2) , C(3) , C(4) , C(5) and C(6) are the low frequency coefficients

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in position 2 to 6 in zigzag scan, F2 is the impact factor of low frequency 0 coefficients, C(x) is watermarked coefficient,Wi0 is the extracted water-

mark bit in selected block.

Step 6. Repeating steps 4 and 5 for extracting all watermark bits and finally obtaining the extracted watermark image.

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6. Performance Evaluation and Experimental Discussion To evaluate the proposed method, we have used six famous images from USC-SIPI image database including Lena, Peppers, Baboon, Barbara, Fishing Boats, F16 with the size of 512×512 pixels as the host images and also an image with size of 32×32 pixels as the watermark image. Host images and watermark 10

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Table 2: Original host images with best position for watermark embedding and the resulting PSNR

Fishing Boats

Barbara

PSNR=41.03

PSNR=40.42

Position(x)=13

Position(x)=13

Position(x)=12

Baboon

Peppers

Lena

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F16

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PSNR=40.07

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PSNR=39.28

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Position(x)=13

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PSNR=40.33

PSNR=40.30

Position(x)=13

Position(x)=10

image are shown in Table2 and Fig.3 respectively. In [1], the most important features of digital watermarking algorithm are introduced as imperceptibility, robustness, security, capacity, reversibility, complexity and possibility of verification. By embedding the 1024 bits watermark into each of the six host images, we have considered the capacity of our proposed method 1024 bits and then

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evaluated the robustness and imperceptibility of our proposed method against various attacks. In next sections the amount of imperceptibility and robustness value of our proposed method will be described respectively.

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Figure 3: Watermark image and its scrambled version

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6.1. Imperceptibility Evaluation

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⇐⇒

Cox et al. define imperceptibility as ”perceptual similarity between the original and the watermarked versions of the cover work” [20]. Imperceptibil-

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ity means that after watermarking implementation, not any visible distortion should appear in the image because it will reduce the commercial value of digi-

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tal media [21]. According to Eq.(6), peak signal to noise ratio (PSNR) is used as a measure for determining the degree of degradation of watermarked image quality compared to the host image [4]. P SN R = 10log10

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n × n × (Xmax )2 Pn Pn ∗ 2 i=1 j=1 (Xi,j − Xi,j )

! (6)

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In which X is the host image, X ∗ is the watermarked image both in n×n and Xmax is peak signal value in the host image. Imperceptibility is associated with

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human sensory properties. A clear watermark causes no quality loss [1]. In general PSNR ≥ 48dB represents that the image quality is excellent, without 220

any noticeable changes. PSNR between 35dB to 48dB means good quality and

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PSNR between 29dB to 35dB represent acceptable quality. PSNR=25dB is the critical point. If the PSNR comes under this amount, in fact watermarking will be obvious [22].

In order to evaluate the imperceptibility of the proposed algorithm, 1024 bits

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of intended watermark is embedded into each of these six host images and PSNR is calculated. Embedding parameters are considered F1=5.49, F2=0.0000014. The obtained PSNR and suitable positions to watermark bits embedding for each host image are represented in Table 2. As can be observed, maximum amount of PSNR is achieved for F16 image with 41.03 dB and minimum value

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is achieved for Baboon image with 39.28 dB and the imperceptibility of other images are between these two values. The obtained results represent that our

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proposed method has high imperceptibility according to presented condition in

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[22]. 6.2. Robustness Evaluation 235

Cox et al. define robustness as ”ability to detect the watermark after com-

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mon signal processing operations” [20]. According to Eq.(7), the normalized correlation (NC) has been used for quantitative evaluation of the similarity be-

(7)

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tween original and extracted watermark [3]: Prw Pcw 0 (Wi,j × Wi,j ) i=1 Prwj=1 Pcw NC = 2 W i,j i=1 j=1

0 In which Wi,j and Wi,j are the original and extracted watermark respectively 240

with size of rw×cw. In general, similarity between the original and extracted

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watermark is achieved with NC> 0.85 [23].

Some researchers use metric BER to evaluate the robustness of watermarking. According to Eq.(8), the bit error ratio (BER) is used to detect the bit

which the zero amount shows no error in extracted watermark [5]:

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error rate in the extracted watermark compared with the original watermark in

BER =

m X n 0 X Wi,j ⊕ Wi,j (m × n) i=1 j=1

(8)

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In which W and W’ are original and extracted watermark image respectively with size of n×m and ⊕ means Xor operation. In the following, the robustness

of our proposed method is investigated under five general groups of attacks. Obtained results after applying 27 various attacks on Lena watermarked image

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are represented in Table 3, Table 4 and Table5. 6.2.1. Filtering Attacks

One of the most common manipulations in digital image is filtering. In this group of attacks we use three famous filters including Median filter, Average filter and Gaussian low-pass filter. In Median filtering, each pixel is replaced by 255

the median of its neighbors, whereas the Average filtering is to replace each pixel with the average value of its neighbors. The pattern of neighbors is called the 13

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Table 3: Robustness of proposed method for Lena host image against first series attacks

Filter

Average

Filter

Median

3×3

3×3

NC

1

0.9724

0.9961

BER

0

0.0136

0.0019

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3×3

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Attacked Image

Extracted water-

50%

Scaling 50%

Left

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Extracted water-

Cropping

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Left-Up

25%

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Cropping Attack

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mark

Attacked Image

Filter

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Gaussian Attack

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mark NC

0.8418

0.7615

0.9862

BER

0.0830

0.1279

0.0068

Attack

Rotation 1 degree

Histogram Sharpening Equalization

Attacked Image

Extracted watermark NC BER

0.9705 0.0146

14

1

1

0

0

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Gaussian Noise

Salt

&

noise

Poisson Noise

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Attacked Image

Extracted water-

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mark 0.9941

1

0.9488

BER

0.0029 0

0

0.0253

Attack

Speckle Noise

AWGN SNR=10

JPEG70+Gaussian

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Noise

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Extracted water-

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NC

Attacked Image

Pepper

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Attack

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Table 4: Robustness of proposed method for Lena host image against second series attacks

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mark NC

0.8780

0.5800

0.9882

BER

0.0605

0.2099

0.0058

Attack

JPEG50+Average JPEG50+Median JPEG70+Scaling50% Filter Filter

Attacked Image

Extracted watermark NC BER

0.9803 0.0097

15

0.9685

0.9862

0.0156

0.0068

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Table 5: Robustness of proposed method for Lena host image against third series attacks

JPEG QF=10

JPEG QF=20

JPEG QF=30

NC

0.9370

1

BER

0.0313

0

0

Attack

JPEG QF=40

JPEG QF=50

JPEG QF=60

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Attack

Extracted water-

NC

M te

mark

1

d

Attacked Image

Extracted water-

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mark

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Attacked Image

0

1

1

BER

0

0

0

Attack

JPEG QF=70

JPEG QF=80

JPEG QF=90

NC

0

1

1

BER

0

0

0

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Attacked Image

Extracted watermark

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”window” and its size is changeable. Also Gaussian low-pass filtering reduces the high frequency components in the signal and produces a version of the

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original image without high frequency components. After performing these

filters on host images, the quality of extracted watermark is evaluated by NC

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and BER. The results are represented in Table 6. As can be seen, robustness of the proposed method in all six host images against Gaussian low-pass filter is

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the maximum possible value. Also the most robustness amount against Median and Average filters belongs to Fishing Boats and F16 images and the minimum 265

amount belongs to Baboon image. In total, the results indicate a very high

an

resistance of the proposed method against these three filters. 6.2.2. Geometric Attacks

M

The robustness of the proposed method against four types of Geometric attacks Cropping 25%, Cropping 50%, Scaling 50%, one degree Rotation are 270

explained in Table 6.

d

Image cropping is frequently used in real life. Cropping refers to the selecting and removing a portion of an image. So some of the embedded watermark bits

te

may be eliminated. The minimum value of robustness against these cropping attacks belongs to Barbara and Fishing Boats host images. Also other host images especially F16 have good robustness.

Ac ce p

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

275

Scaling is commonly performed to fit the image into the desired size, which

causes losing the image information. In Scaling 50%, image size is reduced to 1/2 and again, it is returned to original size. Quality of extracted watermark against this attack is very high, so at the least amount, the NC is equal to

280

0.9744.

Rotation is a circular movement of an input image around a pivot point.

In this attack, host image is rotated by one degree and again returned to the previous state. Then with cutting the main part of image and removing the additional pixels, the least NC value is obtained equal to 0.9705 in the Lena

285

host image.

17

Page 17 of 31

6.2.3. Image Enhancement Attacks This group includes Histogram Equalization and Sharpening attacks. Val-

ip t

ues in Table 6, show that our proposed method has the maximum amount of robustness against these two attacks and the extracted watermark is in the best situation.

cr

290

6.2.4. Compression Attacks

us

Regarding the increasing use of Internet and reducing uploaded images size by users because of low bandwidth, the watermarking algorithm should be ro-

295

an

bust against Compression attacks. Here we have utilized JPEG Compression with quality factor (QF) from 10 to 90. The results in Table 7 indicate that the proposed method has extraordinary robustness against this attack and just

M

with QF=10 some embedded information is lost. 6.2.5. Noise Attacks

300

d

This group of attacks is various and includes five different types of noise, Gaussian noise, Poisson noise, Salt & Pepper noise, Speckle noise, Add White

te

Gaussian Noise (AWGN). In general, noise addition is for degrading the watermark information, which causes difficulty in the watermark extraction. As can be seen in Table 8, the most corrupting influence is given to the watermark-

Ac ce p

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

ing algorithm by white noise with SNR = 10 and in the four other noises, with

305

NC> 0.85 , the obtained results indicate high resistance of the proposed method against these noises.

6.2.6. Combined Attacks

Sometimes, the user executes processing operations consecutively, so investi-

gating the watermarking robustness against combined attacks is important. In

310

our proposed method we have tested four types of combined attacks, including JPEG QF=70 + Gaussian Noise, JPEG QF=70 + Scaling50%, JPEG QF=50 + Average Filter, JPEG QF=50 + Median Filter. Quality of the extracted watermark after applying these types of attacks is investigated in Table 8 and obtained results show high robustness of the proposed method. 18

Page 18 of 31

ip t

Table 6: Resulted NC of proposed method for six host images against first series attacks

Filter

Average 3×3

Lena

1

0.9724

Peppers

1

0.994

Baboon

1

0.9705

Barbara

1

0.9746

Fishing Boats

1

F16

1

Attack

Cropping

0.8418

Peppers Baboon

Fishing Boats

0.9646

0.9764

1

1

1

1

Cropping

50%

Scaling 50%

0.7615

0.9862

0.8318

0.5807

0.9921

0.8502

0.612

0.9744

0.5907

0.4742

0.9764

0.5409

0.4282

1

te

Barbara

0.9902

Left

d

Lena

Filter

0.9961

M

Left-Up

25%

Median 3×3

an

3×3

Filter

cr

Gaussian

us

Attack

Ac ce p

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

F16

0.9236

0.721

1

Attack

Rotation 1 degree

Histogram

Sharpening

Equalization

Lena

0.9705

1

1

Peppers

0.9724

1

1

Baboon

0.9626

1

1

Barbara

0.9665

1

1

Fishing Boats

0.9921

1

1

F16

0.9921

1

1

19

Page 19 of 31

ip t

Attack

JPEG QF=10

JPEG QF=20

Lena

0.937

1

Peppers

0.1301

1

Baboon

0.107

1

Barbara

0.1105

1

Fishing Boats

0.1292

F16

0.1791

Attack

JPEG QF=40

Lena

1

Peppers

1

Baboon

1

Barbara

1

cr

Table 7: Resulted NC of proposed method for six host images against third series attacks

JPEG QF=30

us

1

1

1

1

1

1

JPEG QF=50

JPEG QF=60

1

1

1

1

1

1

1

1

1

1

1

1

1

1

Attack

JPEG QF=70

JPEG QF=80

JPEG QF=90

Lena

1

1

1

Peppers

1

1

1

Baboon

1

1

1

Barbara

1

1

1

Fishing Boats

1

1

1

F16

1

1

1

F16

d

te

Fishing Boats

M

an

1

1

Ac ce p

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

20

Page 20 of 31

ip t

Table 8: Resulted NC of proposed method for six host images against second series attacks

Gaussian Noise

Poisson Noise

Salt

&

Pepper

cr

Attack

noise

1

Peppers

0.9902

1

Baboon

0.9882

1

Barbara

0.9921

1

Fishing Boats

0.9843

F16

0.9862

Attack

Speckle Noise

Peppers Baboon

Fishing Boats

0.9508

0.9607

0.9586

1

0.9077

AWGN SNR=10

JPEG70+Gaussian Noise

0.58

0.9882

0.8427

0.6113

0.994

0.872

0.6078

0.9882

0.9134

0.6113

0.9961

0.9135

0.5937

0.9783 0.9842

te

Barbara

0.935

1

M

0.878

d

Lena

0.9488

us

0.9941

an

Lena

Ac ce p

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

F16

0.6872

0.5665

Attack

JPEG70+

JPEG50+Average JPEG50+Median

Scaling50%

Filter

Filter

Lena

0.9803

0.9685

0.9862

Peppers

0.9902

0.9921

0.9902

Baboon

0.9744

0.9508

0.8957

Barbara

0.9764

0.9707

0.953

Fishing Boats

0.9961

0.9961

0.9941

F16

0.9961

1

1

21

Page 21 of 31

ip t cr us an

Figure 4: The tradeoffs among three main requirements of watermarking

7. Performance Comparison with Other methods

M

315

Among the requirements of the Digital watermarking, three requirements

d

including imperceptibility, robustness and capacity are in conflict with each other and as can be seen in Fig.4 increase in either of these requirements will lead

320

te

to a reduction of two other requirements. Performance of various watermarking methods can be compared with each other in terms of these three requirements. An appropriate way for a fair comparison between watermarking algorithms

Ac ce p

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

is embedding a watermark with the same size as the other algorithms into the common host image. In this case, the capacity value among all algorithms is equaled and necessary conditions for comparison of imperceptibility and robust-

325

ness will be provided. In this section, in order to compare our proposed method with other watermarking algorithms, Lena image is used as host image. Then watermarks with various lengths: 4096 bits (64×64), 1024 bits (32×32), 900 bits (30×30) and 256 bits (16×16) are embedded in Lena host image. At first we embedded a 4096 bits (64×64) watermark in Lena host image

330

and then we compared the proposed method with [4] algorithm. PSNR in our method and in [4] are 33.26 and 33.12 respectively so imperceptibility of our proposed method is higher. As can be seen in Table 9 and Fig.5, robustness of

22

Page 22 of 31

Table 9: Robustness comparison for 2048 bits watermark

Proposed

[4]

Gaussian Filter

5×5

1

0.9895

Average Filter

3×3

0.9876

0.9157

Median Filter

5×5

0.7816

0.8545

Scaling

50%

0.9882

0.9878

0.9916

0.8272

Salt & Pepper

0.9388

0.7256

Speckle Noise

0.8271

0.8132

1

0.988

an

Gaussian Noise

JPEG Compression

QF=50

[4]

M

Proposed 1.2 1

NC

cr

Parameter

us

Attack type

ip t

Based on NC

0.8 0.6

d

0.4 0.2

te

0

Gaussian Filter Average Filter Median Filter 5×5 3×3 5×5

Scaling 50% Gaussian Noise Salt & Pepper Speckle Noise

JPEG QF=50

Figure 5: Comparison between the proposed Proposed [5] method and [4]

Ac ce p 0.3

0.25

the0.2proposed method in all of attacks (except Median filter 5×5) is higher than BER

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

[4].0.15

335

0.1

In the second stage, watermark length is reduced to 1024 bits (32×32). After

0.05

0

Gaussian Cropping Rotation Gaussian JPEG JPEG JPEG10 JPEG JPEG and JPEG also JPEGit that, proposed method is compared withJPEG [5] inJPEG Table and Fig.6 Filter

25%

1 degree

Noise

QF=10

QF=20

QF=30

QF=40

QF=50

QF=60

QF=70

QF=80

QF=90

is compared with [6] in Table 10 and Fig.7. In this case, PSNR of our method is 40.30, while in [5] and [6] are 38.12 and 38.72 respectively. Obtained results show higher PSNR of our proposed method than [5] and [6]. Also regarding

340

robustness, our proposed method has higher resistance especially in compression JPEG and only in Cropping 25%, [5] is better. In compare with [6], our proposed method has higher robustness and just in two Cropping attacks, the way [6] has

23

Page 23 of 31

Proposed

[4]

1.2

0.6 0.4 0.2 0 Gaussian Filter Average Filter Median Filter 5×5 3×3 5×5

Scaling 50% Gaussian Noise Salt & Pepper Speckle Noise

Proposed

JPEG QF=50

[5]

0.3

0.2

0.15 0.1

0.05 0 JPEG QF=20

JPEG QF=30

JPEG QF=40

JPEG QF=50

JPEG QF=60

JPEG QF=70

JPEG QF=80

JPEG QF=90

cr

Gaussian Cropping Rotation Gaussian JPEG Filter 25% 1 degree Noise QF=10

ip t

BER

0.25

Proposed

[6]

1.2

an

NC

1

us

Figure 6: Comparison between the proposed method and [5]

0.8 0.6 0.4

M

0.2

d

0

0.3

te

Figure 7: Comparison between the proposed method and [6] Proposed [7] 0.35

BER

0.25 performance. better 0.2

In the third stage, a watermark with 900 bits (30×30) length is used and the

0.15

Ac ce p

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

NC

1 0.8

0.1

345

obtained results are compared with [7] in Table 11 and Fig.8. PSNR value in 0.05 0

Gaussian Filter Median and Filter [7] Cropping 25% Scaling 41.64 50% Gaussian Noise Salt &The Pepper results JPEG Q=60 our proposed method is 42.06 and respectively. show

that our proposed method has much higher imperceptibility and resistance in all applied attacks.

In the last stage, a watermark 256 bits (16×16) length is embedded in the

350

Lena host image and then the robustness of the proposed method in Table 12 and Fig.9 with [8] and in Table 12 and Fig.10 with [9] are compared. PSNR value in our proposed method, [8] and [9] are 44, 42 and 43 respectively. As can be seen in Table5, our proposed method, in addition to having high imperceptibility, has better robustness than two other methods except AWGN.

24

Page 24 of 31

Table 10: Robustness comparison for 1024 bits watermark

Proposed

[5]

Proposed

[6]

Gaussian Filter

0

0

1

1

Median Filter

-

-

0.9961

0.531

25%

0.0830

0.046

0.8418

0.9908

50%

-

-

0.7615

0.9861

1◦

0.0146

0.074

-

-

-

1

0.9858

-

1

1

0.0029

0.02

0.9941

0.985

-

-

1

1

-

-

0.9488

0.869

QF=10

0.0313

0.274

-

-

QF=20

0

0.231

-

-

0.181

-

-

Cropping

Rotation

-

Sharpening

-

an

Histogram Equalization

Gaussian Noise Poisson Noise

M

Salt & Pepper JPEG Compression

QF=30

0

QF=40

0

0.092

-

-

QF=50

0

0.071

-

-

QF=60

0

0.047

-

-

0.2

QF=70

0

0.01

1

0.9906

0

QF=80

0

0

1

1

QF=90

0

0

1

1

1.2

te

1

NC

[6]

d

Proposed

cr

Parameter

us

Attack type

Based on NC

ip t

Based on BER

0.8 0.6

Ac ce p

0.4

Proposed

[7]

0.35 0.3

0.25

BER

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

0.2

0.15 0.1 0.05 0 Gaussian Filter

Median Filter

Cropping 25%

Scaling 50%

Gaussian Noise

Salt & Pepper

JPEG Q=60

Figure 8: Comparison between the proposed method and [7]

25

Page 25 of 31

Based on BER Parameter

Proposed

[7]

Gaussian Filter

0

0.2147

Median Filter

0

0.3012

cr

Attack type

25%

0.1433

0.2358

Scaling

50%

0.0011

0.1974

Gaussian Noise

0.0156

0.1952

Salt & Pepper

0.0178

0.1027

an

us

Cropping

JPEG Compression

ip t

Table 11: Robustness comparison for 900 bits watermark

QF=60

0

0.1043

M

Table 12: Robustness comparison for 256 bits watermark

Gaussian Filter

[8]

[9]

0

0

0

0

0.0018

-

0

0.0001

0

150%

0

0

-

200%

0

0

-

0.5 ◦

0

0.3653

0.3421

0

-

0.4251

D=0.01

0

0

0

D=0.02

0.0078

-

0.0101

D=0.04

0.0039

-

0.0302

SNR=10

0.1289

0.0179

0.0112

QF=20

0

0.0171

0.0102

QF=30

0

0.0039

0.0014

QF=40

0

-

0.0001

QF=60

0

-

0

Median Filter Scaling

Rotation

Salt & Pepper

AWGN

JPEG Compression

Based on BER

Proposed

te

Average Filter

Parameter

d

Attack type

Ac ce p

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

1

◦

26

Page 26 of 31

[8] [8]

ip t

Proposed Proposed

0

Gaussian Filter Gaussian Filter

Average Filter Average Filter

Median Filter

Scaling 2

Median Filter

Scaling 2

cr

BER BER

0.4 0.35 0.4 0.3 0.35 0.25 0.3 0.2 0.25 0.15 0.2 0.1 0.15 0.05 0.1 0.050

Rotation 0.5 Salt & Pepper AWGN 10 JPEG Q=20 JPEG Q=30 D=0.01 Rotation 0.5 Salt & Pepper AWGN 10 JPEG Q=20 JPEG Q=30 D=0.01

Proposed Proposed

[9] [9]

an

BER BER

0.45 0.4 0.45 0.35 0.4 0.3 0.35 0.25 0.3 0.2 0.25 0.15 0.2 0.1 0.15 0.05 0.1 0 0.05 0

us

Figure 9: Comparison between the proposed method and [8]

M

Gaussian Median Rotation Rotation 1 Salt&Pep Salt&Pep Salt&Pep AWGN Filter Filter 0.5 D=0.01 D=0.02 D=0.04 10 Gaussian Median Rotation Rotation 1 Salt&Pep Salt&Pep Salt&Pep AWGN Filter Filter 0.5 D=0.01 D=0.02 D=0.04 10

JPEG Q=20 JPEG Q=20

JPEG Q=30 JPEG Q=30

JPEG Q=40 JPEG Q=40

JPEG Q=60 JPEG Q=60

8. Conclusion

te

355

d

Figure 10: Comparison between the proposed method and [9]

In this paper, a color space called YCoCg-R, which has been applied rarely so far in watermarking algorithms and its component have a good decorrelation,

Ac ce p

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

is used. In the proposed watermarking method, the relationship between DCT coefficients in Y component of the host image is used for watermark embedding.

360

Among features of this method we can mention utilizing variance function and complexity characteristics for choosing the suitable blocks and also applying mean function to adaptive selection of the embedding strengths. Also in order to increase security, the Arnold transform is performed to scramble the watermark image. To evaluate the proposed method, a watermark with 1024 bits length

365

embedded in six host image and experimental results show high robustness against various attacks especially JPEG compression. Then by changing in watermarking capacity and inserting the watermarks with various lengths, the proposed method was compared with six other algorithms and the results showed

27

Page 27 of 31

that in the same capacity condition, our proposed method has higher robustness than other algorithms.

ip t

370

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cr

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