A new DCT-based robust image watermarking method using teaching-learning-Based optimization

Journal of Information Security and Applications 47 (2019) 28–38

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Journal of Information Security and Applications journal homepage: www.elsevier.com/locate/jisa

A new DCT-based robust image watermarking method using teaching-learning-Based optimization Mohammad Moosazadeh a,∗, Gholamhossein Ekbatanifard b a b

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

a r t i c l e

i n f o

Article history:

MSC: 68U10 Keywords: Robust image watermarking Desert cosine transform (DCT) JPEG-YCbCr color space JPEG Compression Teaching-Learning-Based optimization (TLBO)

a b s t r a c t Despite the passage of more than 20 years of raising the issue of watermarking by Tirkel, the researchers are still seeking to provide an approach more resistant than existing solutions. In this paper we proposed a new watermarking method that works on JPEG-YCbCr color space and the embedding operation is based on the relationships between the DCT coefficients. The JPEG-YCbCr is rescaling of YCbCr color space that has a good robustness against most of the attacks and it used in JPEG image format. Also the relationship between the DCT coefficients is stable against most of the changes in the host image. Therefore, the proposed method has more robustness compared to some other methods. On the other hand, many intelligent optimization methods are in use regarding the nature of the phenomenon simulated by the methods. Teaching-Learning-Based Optimization (TLBO) is a novel method of optimization which has become a hot issue in recent years. The algorithm works on the principle of teaching and learning, where teachers increase the knowledge of students and also the students learn from interaction among themselves. The proposed method uses TLBO which has been applied rarely so far in watermarking algorithms and it can automatically determine the embedding parameters and suitable position for inserting the watermark. Besides, in the object function of TLBO, ensuring higher imperceptibility and also robustness against Median filter and JPEG compression have been considered. According to the experimental results, the imperceptibility of watermarked images is satisfactory, and embedded watermark is extracted successfully even if the watermarked image is distorted by various attacks. © 2019 Elsevier Ltd. All rights reserved.

1. Introduction In recent years, the rapid growth of Internet technology and multimedia have made the websites and social networks a useful tool in our life. On the other hand, easy access to image processing software has made it easy to tampering, illegal distribution and copying such multimedia data. Therefore, as a possible solution to this issue, digital watermarking is used to protect the intellectual property rights. Digital watermarking embeds digital copyright information (e.g. logo or a pseudo-random sequence) in the target media which can be invisible. The first watermarking algorithm was illustrated by Tirkel, Osborne and Rankin in 1993 [1]. Digital watermarking algorithms generally include two domains: spatial domain in which digital watermark is added to image pixel values directly, and frequency domain that embeds the digital watermark by changing the values of the host image transformation

∗

Corresponding author. E-mail addresses: [email protected] [email protected] (G. Ekbatanifard). https://doi.org/10.1016/j.jisa.2019.04.001 2214-2126/© 2019 Elsevier Ltd. All rights reserved.

(M.

Moosazadeh),

coefficients. Some famous frequency domain methods are Discrete Cosine Transform (DCT), Discrete Fourier Transform (DFT), and Discrete Wavelet Transform (DWT). Also watermarking can be divided into two categories consisting of robust watermarking for copyright protection and fragile watermarking for authenticating [2]. Most of the previously developed watermarking methods usually determine their parameters experimentally. Although, because watermarking algorithms have large parameter space, it is usually difficult to experimentally determine optimal watermarking parameters. A good solution for this problem is to regard it as an optimization problem. Hence, metaheuristic optimization techniques (also called advanced optimization techniques) have emerged as a considerable tool in recent years. Considering the nature of the phenomenon, Rao et al. [3] have divided the population-based heuristic algorithms into two different groups: evolutionary algorithms (e.g. Genetic Algorithm) and swarm intelligence algorithms (e.g. Artificial Bee Colony). The main limitation of all the mentioned algorithms is having algorithm-specific parameters that tuning these parameters is important for finding the optimum solution and it is an optimization problem itself [3]. Hence for

M. Moosazadeh and G. Ekbatanifard / Journal of Information Security and Applications 47 (2019) 28–38

solving this problem, Rao et al. [4–6] presented a new optimization algorithm known as “Teaching-Leaning-Based Optimization (TLBO)” algorithm which requires only the common control parameters like population size and number of generations for its working. For it does not require any algorithm-specific parameter to be tuned, its implementation is simpler than others algorithms. To date, several watermarking methods have been proposed by using the metaheuristic optimization techniques. Moosazadeh and Andalib [7] presented a watermarking scheme based on DCT and genetic algorithm (GA) in which GA is applied to select the target coefficients of the host image. Wang et al. [8] presented a multi-objective genetic algorithm (GA) based image watermarking method. They used a multi-objective genetic algorithm with a variable-length mechanism to automatically optimize the watermarking parameters and search the most suitable positions for embedding watermark bits. Ebrahimi Moghaddam and Nemati [9] proposed a robust watermarking technique using Imperialistic Competition Algorithm (ICA) in the spatial domain where the ICA is used to find a suitable location for watermark embedding in different color channels. In Agarwal et al. [10], a hybrid GA-BPN intelligent network based watermarking scheme was proposed, in which the HVS characteristics of four host images in DCT domain are used to obtain a sequence of weighting factor from a GA-BPN. Then this weighting factor is used to embed a binary watermark image in the host image in the DWT domain in LL3 sub band. In Horng et al. [11], a blind image watermarking method is introduced through a hybridization of DCT and SVD based on GA where in the singular value of DCT-transformed host image is modified with the quantizing value that is found using GA. Maity et al. [12] proposed a collusion resilient optimized spread spectrum image watermarking scheme by using genetic algorithms (GA) and multiband wavelets where the GA was employed to determine threshold value of the host image coefficients (process gain e.g. the length of spreading code) and the respective embedding strengths compatible to the gain of frequency response. Also they proposed in paper [13] an multicarrier spread spectrum image watermarking algorithm using hybridization of genetic algorithms (GA) and neural networks (NN) where the GA selects appropriate gradient thresholds for partitioning the host image and calculating the embedding strengths. The NN, as well, calculates the weight factor in minimum mean square error combining (MMSEC) to improve the watermark decoding performance and interference cancelation. In Ali et al. [14], a watermarking scheme based on differential evolution (DE) in discrete wavelet transform-singular value decomposition (DWT-SVD) transform domain is proposed where the DE is used to search optimal scaling factors for improving imperceptibility and robustness. Peng et al. [15] introduces a ridgelet based image watermarking algorithm, and then develops a novel watermarking framework based on tissue P systems in which a special membrane structure is designed and its cells are used as parallel computing units to find the optimal watermarking parameters. Ansari et al. [16], introduced a secure optimized image watermarking ABC scheme in which the values of scaling factors are found with the help of artificial bee colony (ABC). In Abdelhakim et al. [17], a recent watermarking scheme is utilized as the embedding algorithm and also the Artificial Bee Colony (ABC) is selected as the optimization method in which the fitness function is used. The fitness function is based on dividing the problem into two single objective optimization sub-problems in which the robustness and imperceptibility objectives are optimized separately. So, there is no need for weighting factors. Ansari and Pant [18] proposed an image watermarking scheme in order to prepare tampering detection and ownership verification in which main components of watermark is used to robust watermark embedding and the last two LSB of host image is applied for the fragile watermark embedding. The robust insertion is optimized with the help of Arti-

29

ficial Bee colony (ABC) by optimization of the scaling factors. In paper [19], a semi blind image watermarking scheme in wavelet domain based on Artificial Bee Colony (ABC) is proposed where the encrypted watermark is embedded into wavelet coefficients by utilizing reference image generated by using SVD and scaling factor. The ABC method is employed to optimize the scaling factor. Yazdan Bakhsh and Ebrahimi Moghaddam [20] presented a robust watermarking method in wavelet domain for High Dynamic Range Images (HDRIs) in which ABC is applied to seek the appropriate block for watermark embedding. Takore et al. in paper [21], proposed a robust image watermarking scheme using LWT, DCT and SVD in which Particle Swarm Optimization (PSO) algorithm has been adopted to obtain optimized multiple scaling factors, which can achieve the best trade-off between imperceptibility and robustness. In this paper we describe a DCT-based image watermarking scheme [2,7,22] that uses the relationship between frequency coefficients from each block to embed the watermark bits. This selected relationship is stable against most of the changes and it can guarantee the robustness of proposed method. As well as, a color space called JPEG-YCbCr is used, that its components have a good decorrelation [23,24]. Among features of this method we can mention utilizing variance function (complexity feature) for choosing the suitable blocks and applying TLBO method which has been used rarely so far in watermarking algorithms to optimize the selection of watermarking parameters. Regarding the watermark embedding method, watermarking parameters and embedding positions are important factors that affect the performance of a watermarking system. So we decided to solve this problem by using an efficient optimization algorithm. The proposed image watermarking method can automatically determine optimized watermarking parameters and the most suitable embedding positions for inserting the watermark bits. The remaining part of this paper is organized as follows: In Sections 2 and 3 preliminaries and proposed image watermarking algorithm are described respectively. Performance evaluation and experimental discussion of the proposed method is presented in Section 4 and performance comparison is given in Section 5. Finally, the conclusion of this paper is drawn in Section 6. 2. Preliminaries 2.1. Discrete cosine transform There are various transformation methods for transforming spatial domain to frequency domain. One of them is Discrete Cosine Transform or DCT that used for robustness of watermarking. It decomposes the obtained coefficients into three different frequency bands, including high, middle and low frequency bands by using zigzag scan. The most energy of a signal corresponds to the low frequency band that is perceptually significant portion of image, so any change in this part causes a reduction in signal quality. Also the high frequency band has the lowest energy and they are very week against attacks. By considering the features of the image, we can insert the watermark bits into one of these bands. According to the above mentioned content, the watermark is usually inserted into the middle band. DCT has good robustness against image enhancement operations such as filtering, brightness and contrast adjustment, blurring etc. But, it is fragile against geometric operations such as rotation, scaling and cropping [25]. 2.2. JPEG-YCbCr color space YCbCr is a color model in which the Y component represents the luminance or brightness (luma), while chrominance components include Cb and Cr specify blue difference and red difference

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M. Moosazadeh and G. Ekbatanifard / Journal of Information Security and Applications 47 (2019) 28–38

respectively. This model is considered as offset version of YUV, but its concept is different. YUV is analog system while YCbCr is digital system. YCbCr is used in image and video compression such as MPEG-1, MPEG-2, MPEG-4, H.261, H.262, H.263, H.264, JPEG, JPEG20 0 0 etc [26]. To gain more resistance against most of the attacks in the JPEG image format, JPEG-YCbCr color space is used. This color space is a rescaling of YCbCr with Y, Cb and Cr in [0,1]. The RGB to JPEGYCbCr transformation and its inverse are defined as Eqs. (1) and (2) respectively [23]:

 

Y Cb = Cr

  R G B





=





0 0.29890 0.58660 0.11450 0.5 + −0.16874 −0.33126 0.50 0 0 0 0.5 0.50 0 0 0 −0.41869 −0.8131

1 0 0.140200 1 −0.34414 −0.71414 1 1.7720 0

 





Y 0 Cb − 0.5 Cr 0.5

  R G B

(1)

(2)

  Where, X j,k,i is the updated value of Xj,k,i . X j,k,i is accepted if it gives better object function value. At the end of the teacher phase, all the accepted object function values become the input to the student phase. So the student phase depends upon the teacher phase.

2.3.2. Student phase In the second part of the TLBO algorithm, the student phase is performed where students enhance their results by interacting among themselves. In this phase, students try to learn from each other, so each student interacts randomly with other students. A student learns new things, if another student has better result. The details of this phase are explained below. Two students P and Q are randomly selected such that their  results at the end of the teacher phase are not the same (Xtotal−P,i =  Xtotal−Q,i ). Then their results are updated by Eqs. (6) and (7).  = X  + r (X  − X  ), X j,P,i i j,P,i j,P,i j,Q,i

  I f Xtotal−P,i < Xtotal−Q,i

(6)

 = X  + r (X   X j,P,i i j,P,i j,Q,i − X j,P,i ),

  I f Xtotal−Q,i < Xtotal−P,i

(7)

2.3. Teaching-Learning-Based optimization (TLBO) The TLBO algorithm proposed by Rao et al. [4–6] and Rao and Savsani [27]. This algorithm has been inspired by teaching-learning process (see [3] for more details). The TLBO algorithm is based on the capability of a teacher in teaching the students in a classroom. In this method, a student with highest mark is generally considered as a teacher sharing his or her knowledge with other students. Since the quality of a teacher affects the results of students, it is clear that a good teacher teaches students in a way that they can get better marks. Moreover, students also learn from each other which help in improving their results. The algorithm includes two phases: learning through teacher and learning through interaction with the other students. In TLBO, students and various subjects presented to the students are considered as population and design variables of the optimization problem respectively. A student’s result, as well, is similar to the ‘fitness’ value of the optimization problem and the student with highest mark (best student in the whole population) is considered as the teacher. In fact, the design variables are the parameters involved in the object function and the best student has the best value of the object function. The phases of the algorithm are described below. 2.3.1. Teacher phase After defining and initializing the design variables, teacher phase is performed as the first part of the TLBO algorithm where students learn through the teacher. In this phase, a teacher tries to increase the mean result of the class depending on his or her capability. Assume that there are ‘m’ subjects (as design variables) and ‘n’ students (as population size). At any iteration i, the result of each student (k) is calculated. Then a student with the best result (kbest ) is considered as the teacher who trains other students so that they can have better results. After that, the mean result of the students in a subject j (Mj,i ) is obtained. Then the difference between the mean and the result of the teacher for each subject is given by Eq. (3),

Di f f erence_Mean j,k,i = ri (X j,kbest,i − TF M j, i )

(3)

Where, ri is the random number in the range (0, 1), Xj,kbest,i is the result of the best student in subject j and TF is the teaching factor which decides the value of mean to be changed. The Value of TF can be either 1 or 2 and its value is randomly decided by the algorithm using Eq. (4).

TF = round[1 + rand (0, 1 ){2 − 1}]

(4)

Then in order to move the existing students (Xj,k,i ) toward the teacher, it is updated according to Eq. (5).  X j,k,i = X j,k,i + Di f f erence_Mean j,k,i

(5)

X 

is accepted if it gives a better object function value. The j,P,i Eqs. (6) and (7) are for minimization problems. In the case of maximization problems, the Eqs. (8) and (9) are used.  = X  + r (X  − X  ), X j,P,i i j,P,i j,P,i j,Q,i

  I f Xtotal−Q,i < Xtotal−P,i

(8)

 = X  + r (X   X j,P,i i j,P,i j,Q,i − X j,P,i ),

  I f Xtotal−P,i < Xtotal−Q,i

(9)

3. Proposed image watermarking algorithm The watermarking scheme is divided into three stages: Watermark embedding algorithm, Watermark extraction algorithm and optimization procedure. In the following, we describe the proposed method in detail: 3.1. Watermark embedding algorithm The block diagram of the proposed watermark embedding is described in Fig. 1. At first the host image is transformed from RGB to JPEG-YCbCr color space and the three components Y, Cb and Cr are separated. Then the Y component is selected for watermark embedding, and DCT transform and 8 × 8 blocking are performed. At the third step, the complexity of each block is calculated by variance function and the most complex blocks are selected for watermark embedding. After that, the zigzag scan is used in order to dividing each block into low, middle and high frequency bands and then embedding operations is performed by modulating the relationship between the frequency coefficients according to Eq. (10):

R = (C(2) + C(3) + C(4) + C(5) + C(6) ) × F



C( x ) =

R − α i f R − C(x ) < α and Wi = 0 R + α i f C(x ) − R < α and Wi = 1 C(x ) otherwise

(10)

In which C(2) , C(3) , C(4) , C(5) and C(6) are the low frequency coefficients in position 2–6 in zigzag scan, R is the embedding threshold value of the host image coefficients, F is the impact factor of the low frequency coefficients on determining the threshold, C(x) is the target coefficient for embedding, α is the embedding strength, Wi is corresponding watermark bit and C( x ) is watermarked coefficient in the selected block. After embedding all watermark bits, watermarked Y component is returned from frequency domain to spatial domain by inverse DCT and watermarked Y component is combined with two other original components. At the end, the image is transformed from JPEG-YCbCr to RGB color space and the watermarked image is obtained.

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Fig. 1. Watermark embedding model.

Table 1 TLBO parameters.

3.2. Watermark extraction algorithm At first the watermarked image is transformed from RGB to JPEG-YCbCr color space and the three components Y, Cb and Cr are separated. Then the Y component is selected for watermark extraction, and DCT transformation and 8 × 8 blocking are performed. In the next step, the watermarked blocks are selected based on the initial sorting. After that, the zigzag scan is used in order to dividing each block into low, middle and high frequency bands and then extraction operations is performed by investigating the relationship between the frequency coefficients according to Eq. (11):

R = (C( 2) + C( 3) + C( 4) + C( 5) + C( 6) ) × F



Wi =

1 i f C( x ) > R 0 otherwise

(11)

In which C( 2 ) , C( 3 ) , C( 4 ) , C( 5 ) and C( 6 ) are the low frequency coefficients in position 2–6 in zigzag scan, R is the embedding threshold value of the host image coefficients, F is the impact factor of low frequency coefficients on determining the threshold, C( x ) is watermarked coefficient, Wi is the extracted watermark bit in the selected block. Finally the extracted watermark image is obtained. 3.3. Optimization procedure using TLBO In this paper we applied a TLBO algorithm to solve the optimal image watermarking problem with less computational effort and high consistency. It can be seen in embedding algorithm, some parameters (Position x, Alpha and F) are experimentally selected, so because of depending the results on these parameters, it can affect the watermarking output. Therefore finding an optimal value of these parameters is needed. So we use the TLBO algorithm to automatically determine them. After optimization, the TLBO will generate best Alpha and F and the most suitable embedding position which improve imperceptibility and robustness of watermarking. The imperceptibility and robustness are defined as below:

Parameter

Description

nStu MaxIt MinPosition MaxPosition MinAlpha MaxAlpha MinF MaxF

Number of students (population size) Maximum number of iterations Lower boundary to the Position Upper boundary to the Position Lower boundary to the Alpha Upper boundary to the Alpha Lower boundary to the F Upper boundary to the F

Cox et al. define imperceptibility as “perceptual similarity between the original and the watermarked versions of the cover work” [28]. According to Eq. (12), to gain imperceptibility, peak signal to noise ratio (PSNR) is used in which the host image is compared to the watermarked image [22]:



P SNR = 10 log10

2552 × NM

N−1 M−1 x=0

y=0

( f (x, y ) − f  (x, y ))2



(12)

In which f is the host image, f’ is the distorted image both with size N × M and 255 is peak signal value in the host image. Imperceptibility is related to human visual system (HVS). Also Cox et al. define robustness as “ability to detect the watermark after common signal processing operations” [28]. According to Eq. (13), the normalized correlation (NC) is applied for comparing between original and extracted watermark [29]:

 

NC =

i

 j W (i, j )W (i, j )   2 i j [W (i, j )]

(13)

In which W(i,j) and W’(i,j) are the original and extracted watermark respectively with size of m × n. In the following, we will describe the optimization procedure steps in detail and the block diagram of TLBO is shown in Fig. 2: Step 1. Initializing the design variables mentioned in Table 1. Step 2. Producing nStu random students based on design variables as initial population.

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M. Moosazadeh and G. Ekbatanifard / Journal of Information Security and Applications 47 (2019) 28–38

Fig. 2. Block diagram of TLBO.

Step 3. Assigning fitness value to each initial student by performing the object function. The object function is defined as Eq. (14):

Ob jectF unction = P SNR +

N

λi .NCi

(14)

i=1

Where the PSNR means watermarking imperceptibility, NCi means robustness of the proposed method against ith attack, λi is a weighted factor and N is the number of attacks. Step 4. Performing Teacher Phase: (a) Identifying the Teacher by selecting the best student based on fitness value. (b) Calculating the mean of each design variable (MeanPosition, MeanAlpha, MeanF). (c) Improving all the students in Teacher Phase by the following steps (Do for nStu times): (i) Determining the steps based on the parameters of Teacher as Eq. (15):

StepPosition = T eacher.Position − TF × MeanPosition StepAl pha = T eacher.Al pha − TF × MeanAl pha StepF = T eacher.F − TF × MeanF (15) (ii) Creating a new student by modifying the current student according Eq. (16):

NewSt u.Posit ion = round (St u.Posit ion + rand (0, 1 ) × StepPosition ) NewStu.Al pha = round (Stu.Al pha + rand (0, 1 ) × StepAl pha ) NewStu.F = Stu.F + rand (0, 1 ) × StepF (16) (iii) Assigning fitness value to the new student by evaluating object function. (iv) Comparing two students, if the new student is better than the current student, then replacing the current, or not keeping it as it is. Step 5. Performing Student Phase: (a) Improving all students in Student Phase by following steps (Do for nStu times):

(i) Selecting two students (FirstStu and SecondStu) randomly. (ii) Determining steps based on differencing between two selected students as Eq. (17):

F irstStu.F it > SecondStu.F it StepPosition = F irstSt u.Posit ion − SecondSt u.Posit ion StepAl pha = F irstStu.Al pha − SecondStu.Al pha StepF = F irstStu.F − SecondStu.F Elsei f SecondStu.F it > F irstStu.F it StepPosition = SecondStu.Position − F irstStu.Position StepAl pha = SecondStu.Al pha − F irstStu.Al pha StepF = SecondStu.F − F irstStu.F If

(17) (iii) Create a new student by modifying the First student according Eq. (18):

NewSt u.Posit ion = round (F irstSt u.Posit ion + rand (0, 1 ) ×StepPosition ) NewStu.Al pha = round (F irstStu.Al pha + rand (0, 1 ) × StepAl pha ) NewStu.F = F irstStu.F + rand (0, 1 ) × StepF (18) (iv) Assigning fitness value to the new student by evaluating object function. (v) Comparing two students, if the new student is better than the current student, then replacing the current, or not keeping it as it is. Step 6. Repeating Teacher and Student Phases (steps 4 and 5) for MaxIt times. Step 7. At the end, identifying the best student based on the fitness value and reporting it as the best solution to improve the mentioned image watermarking algorithm. 4. Performance evaluation and experimental discussion This section presents the performance of the proposed method in terms of imperceptibility and robustness. To evaluate the proposed method, we have used ten famous color images from

M. Moosazadeh and G. Ekbatanifard / Journal of Information Security and Applications 47 (2019) 28–38

33

Table 2 Original host images.

Lena

Peppers

Baboon

Barbara

Fishing boats

F-16

House

Splash

Tiffany

Sailboat on lake

Table 4 The result of the TLBO optimization.

Fig. 3. Watermark image and its scrambled version. Table 3 Initializing the TLBO parameters. Parameter

Value

nStu MaxIt MinPos MaxPos MinAlpha MaxAlpha MinF MaxF Object Function attacks

50 100 7 28 30 40 0.001 0.1 JPEG compression with quality factor 10 Median filter with window size of 3 × 3 λ1 =20 λ2 =20

weighted factors

USC-SIPI image database including Lena, Peppers, Baboon, Barbara, Fishing boats, F-16, House, Splash, Tiffany, Sailboat on lake with the size of 512 × 512 pixels as the host images and also the image of Mehrastan University’s logo with the size of 32 × 32 pixels as the watermark image. In Table 2 and Fig. 3 the host images and the watermark logo are shown respectively. As we know, imperceptibility, robustness and capacity are the most important features of digital watermarking algorithms. The proposed technique inserts a watermark of size 32 × 32 (1024) bits into each of the ten host images. So we have considered the capacity of our proposed method 1024 bits and then evaluated the imperceptibility and robustness against various attacks. The TLBO optimization parameters initialization is listed in Table 3. We have selected the numbers of the students and the iterations equal to 50 and 100, respectively. The increase in these values leads to an increase in the accuracy of the results, but on the other hand, the time complexity of the optimization algorithm will increase. We have also determined the upper and lower limits of the other parameters (Position, Alpha and F) based on the experience. Then, the exact values of them will be determined by TLBO.

Host Image

Lena

Peppers

Baboon

Barbara

Fishing boats

Best Position(x) Best Alpha Best F

9 36 0.004

8 37 0.002

12 33 0.059

9 35 0.032

9 34 0.005

Host Image Best Position(x) Best Alpha Best F

F-16 27 38 0.003

House 16 36 0.071

Splash 7 30 0.016

Tiffany 24 38 0.093

Sailboat on lake 8 37 0.013

Two different attacks are applied for object function. The first attack is the JPEG compression with quality factor 10 and the second attack is Median filter with window size of 3 × 3 that our proposed method is weak against them. The optimization objective is to maximize the robustness against these two attacks. An increase in the number of attacks will increase the execution time of the process. The optimization performance for the values of Position, Alpha and F for ten different host images obtained after 100 iterations is shown in Table 4. In the next sections the amount of imperceptibility and robustness value of our proposed method will be described respectively. 4.1. Imperceptibility evaluation As we mentioned in Section 4, imperceptibility is related to human visual system (HVS). To gain imperceptibility, peak signal to noise ratio (PSNR) is used in which the host image is compared to the watermarked image. In general, excellent quality is achieved by PSNR ≥ 48 dB indicating that the image has not any changes and the PSNR between 35 dB to 48 dB means good quality. PSNR between 29 dB to 35 dB shows acceptable quality. The critical point is occurred in PSNR = 25 dB that means watermarking will be obvious [30]. In order to evaluate the imperceptibility of the proposed method, at first the embedding parameters should be selected. So the embedding parameters (Position, Alpha and F) are automatically determined by TLBO like Table 4. After that, 1024 bits of the watermark is embedded into each of these ten host images and then the PSNR is calculated. The obtained PSNR for each host image is represented in Table 5. Numerical values show that

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Table 5 Evaluation the imperceptibility of proposed method for ten host images in terms of PSNR. Host Image

Lena

Peppers

Baboon

Barbara

PSNR (dB)

40.35

40.26

40.19

40.32

Fishing boats 40.73

Host Image PSNR (dB)

F-16 40.63

House 40.76

Splash 39.95

Tiffany 40.61

Sailboat on lake 40.22

the proposed TLBO based watermarking method offers suitable imperceptibility. As it can be seen in Table 5, the maximum and minimum PSNR belong to House with 40.76 dB and Splash with 39.95 dB respectively. 4.2. Robustness evaluation As we said in Section 4, the normalized correlation (NC) is applied for evaluating the robustness of watermarking scheme by comparing the original and the extracted watermark. Totally, NC > 0.85 shows high similarity between the original and extracted watermark. Another metric to evaluate the robustness of watermarking is BER which is used to describe the number of incorrectly extracted watermark bits divided by the total number of watermark bits [31]. The BER is defined as Eq. (19):

P BER(W, W  ) =

i=1

Q

|Wi j − Wij | j=1 P×Q

,

(19)

In which W and W’ are the original and extracted watermarks respectively, P × Q is the watermark image dimension. In the following, the proposed watermarking algorithm has been tested to check the robustness under six general groups of attacks. The results of performing 36 various attacks on watermarked image of Lena are shown in Tables 6 and 7. 4.2.1. Robustness against filtering attacks Image filtering is one of the most common image processing techniques that is used to remove the various types of noise and smooth the finer details. In this group of attacks, watermarked image is passed through six different types of filter include Gaussian low-pass, Average, Median, Wiener, Disk and Motion filters with various parameters. Median and Average filters are two filters that respectively replace median and average of the neighboring pixels to the input pixel. The Gaussian low-pass filter passes components with a frequency lower than a given cutoff frequency. Wiener filtering executes the deconvolution by inverse filtering and removing the additive noise by a low-pass filtering simultaneously. The pattern of neighbors is called the “window” and its size is changeable. Disk filter returns a circular averaging filter in which the value of each pixel is replaced by the mean of the values of all pixels inside a circle around this pixel. Also the Motion filter creates a linear motion in a direction determined by the “Angle” option in a counterclockwise direction. The filter becomes a vector for horizontal and vertical motions. The default direction is horizontal to the right. The results in Table 8 represent the proposed method in Lena, Peppers, Barbara, Boats, Tiffany and Sailboat host images has a very high resistance against filtering and in F-16, House and Splash has a reduction against these attacks. 4.2.2. Robustness against image enhancement attacks This group includes Histogram Equalization and Sharpening attacks. Histogram equalization is used to contrast adjustment and Sharpening is performed to clarify the details of the image. The results in Table 8 show that the proposed method is not sensitive to these attacks, so the extracted watermark is in the best situation.

4.2.3. Robustness against geometric attacks In this group, we applied three types of geometric attacks: Cropping, Scaling and Rotation correction. Cropping is the process of removing some parts of an image on different sizes and shapes that may contain watermark information. According to Table 8 the minimum value of robustness against Cropping 20 pixels from each side attack belongs to House host image and against Cropping 1/4 Up-Left and Cropping 1/2 Left attacks belongs to Barbara and Fishing Boats host images. Also other host images have good robustness. Rotation is a circular movement of an input image around a pivot point. To test our algorithm for Rotation attack, the watermarked image is rotated by 1◦ , 75◦ and 180◦ in clockwise direction separately and then for the extraction of watermarks the rotated image is re-rotated in counter clockwise direction. Based on results in Table 8, all of the host images are robust against rotation except F-16 and House. Scaling is shrinking the watermarked image from the original size to a desired size. The robustness of the proposed method is also investigated for scaling attack with a scaling factor 1/2 where the watermarked images are scaled from 512 × 512 to 256 × 256 pixels. For correct extraction, the scaled image rescaled from 256 × 256 to 512 × 512 pixels. According to Table 8, the obtained NC is located between 0.7871 and 0.9901, so the quality of extracted watermark is high. 4.2.4. Robustness against noise attacks Addition of channel noise frequently occurs during image transmission. To test the effectiveness of our proposed scheme against noise attacks, we have applied five different types of noise include Gaussian, Poisson, Uniform, Salt & Pepper and Speckle noises that are used to degrading the watermark information. As can be seen in Table 9, in the all five noises, the proposed scheme has good robustness. Of course the robustness against Salt & Pepper has a little reduction. 4.2.5. Robustness against compression attacks JPEG compression is a universal format of image compressing that can reduce the image storage requirement and image transmission bandwidth. This compression technique is applied with different quality factors ranging from 0 to 100. The value of quality factor 100 represents a high quality image while reducing the amount of quality factor causes decreasing the quality of image. In the current work we compress the watermarked images using JPEG compression with quality factor (QF) from 10 to 90. The results in Table 9 indicate that the proposed method has high robustness against this attack. 4.2.6. Robustness against combined attacks Besides being robust against the singular attacks, the proposed method should be robust against combined attacks. Because sometimes several attacks may occur consecutively, hence 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. As can be seen in Table 9, the proposed method has acceptable robustness against mentioned attacks. 5. Performance comparison As we mentioned in previous section, the most important requirements of digital watermarking algorithm are imperceptibility, robustness and capacity which are in conflict with each other. Hence increasing either of these requirements will lead to decreasing two others. So, when comparing the performance of watermarking methods, it is important to pay attention to this issue. A convenient way to fairly compare the performance of two watermarking algorithms includes the following steps:

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Table 6 Watermarked image of Lena and extracted watermark after first series attacks.

Step 1. Using a common host image. Step 2. Equalizing the capacity value in both methods by using a watermark with the same size. Step 3. Comparing imperceptibility and robustness of two methods.

To perform the first step, the Lena image is commonly used. To run the second step, a watermark of the same size is used, so that the capacity of the two methods is equaled. Now in the third step, by applying different attacks with the same parameters, one

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Table 7 Watermarked image of Lena and extracted watermark after second series attacks.

Table 8 Evaluation the robustness of proposed method against filtering, enhancement and geometric attacks for ten host images in terms of NC. Attack

Gaussian filter 3×3

Gaussian filter 5×5

Average filter 3×3

Average filter 5×5

Median filter 3×3

Median filter 5×5

Wiener filter 3×3

Disk filter (Blurring)

Motion Filter (Blurring)

Lena Peppers Baboon Barbara Boats F-16 House Splash Tiffany Sailboat

1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1

0.9862 1 0.9369 0.9607 0.9823 0.412 0.3057 0.8866 0.9355 1

0.8524 0.8681 0.1576 0.793 0.8204 −0.9704 −0.8942 0.2707 0.5769 0.8779

0.9842 0.9862 0.9232 0.9645 0.9842 −0.0377 −0.0703 0.9186 0.9727 0.9862

0.8857 0.9015 0.2775 0.8344 0.8605 −0.9036 −0.9004 0.5863 0.6558 0.9134

1 1 1 1 1 0.4763 0.3663 0.9267 0.9611 1

1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 0.4998 0.3421 0.9507 1 1

Attack

Histogram Equalization

Sharpening

Cropping 25% Up-Left

Cropping 50% Left

Rotation 1◦

Rotation 75◦

Rotation 180◦

Scaling 50%

Lena Peppers Baboon Barbara Boats F-16 House Splash Tiffany Sailboat

1 1 1 1 0.9746 1 1 0.9502 1 1

1 1 1 1 1 1 1 1 1 1

Cropping 20 pixels each side 0.8704 0.7831 0.8525 0.9054 0.8919 0.8587 0.7827 0.8124 0.8996 0.9292

0.7778 0.8193 0.8379 0.578 0.5274 0.8879 0.7868 0.9174 0.8719 0.6708

0.6606 0.5496 0.5631 0.4928 0.4513 0.6508 0.6922 0.6591 0.7271 0.5639

0.9646 0.9646 0.8995 0.9607 0.9862 0.6279 0.7832 0.9358 0.9665 0.9764

0.9448 0.9252 0.7509 0.9251 0.9411 0.6037 0.6117 0.8917 0.6901 0.9468

1 1 1 1 1 1 1 1 1 1

0.9862 0.9901 0.9291 0.9705 0.9744 0.9685 0.7871 0.9038 0.8521 0.9862

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Table 9 Evaluation the robustness of proposed method against noise, compression and combined attacks for ten host images in terms of NC. Attack

Gaussian Noise

Poisson Noise

Uniform noise S = 0.005

Salt & Pepper noise

Speckle noise

JPEG70 + Gaussian noise

JPEG70 + Scaling 50%

JPEG50 + Average filter

JPEG50 + Median filter

Lena Peppers Baboon Barbara Boats F-16 House Splash Tiffany Sailboat

0.9548 0.9763 0.9429 0.9685 0.9665 0.97648 0.9586 0.9349 0.91556 0.9588

1 1 1 1 1 1 1 1 1 1

0.9133 0.8702 0.8528 0.9153 0.9014 0.9055 0.8925 0.8537 0.8017 0.8881

0.8672 0.8073 0.8286 0.8744 0.8612 0.7514 0.7236 0.7555 0.5528 0.7877

1 1 1 1 1 1 1 1 1 1

0.9567 0.9764 0.9527 0.943 0.941 0.9488 0.9057 0.9152 0.862 0.9724

0.9783 0.9882 0.9212 0.9705 0.9666 0.9586 0.7641 0.9097 0.8541 0.9843

0.9862 0.9961 0.9232 0.9588 0.9823 0.819 0.8257 0.8862 0.9239 0.9902

0.9862 0.998 0.8858 0.9568 0.9921 0.8028 0.8189 0.9248 0.9569 0.9902

JPEG compression attacks Attack

QF = 10

QF = 20

QF = 30

QF = 40

QF = 50

QF = 60

QF = 70

QF = 80

QF = 90

Lena Peppers Baboon Barbara Boats F-16 House Splash Tiffany Sailboat

1 0.9708 0.8245 0.9862 0.9783 0.7891 0.8106 0.8072 0.8314 0.9921

1 1 1 1 1 0.8781 0.9114 0.9981 0.8963 1

1 1 1 1 1 0.9749 1 1 0.9176 1

1 1 1 1 1 1 1 1 0.9667 1

1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1

Table 10 Robustness comparison after 1024 bits watermark embedding into Lena with the size of 512 × 512. Attack type

PSNR (dB) Histogram Equalization Sharpening JPEG Compression Gaussian Noise Poisson Noise Speckle Noise Salt & Pepper Noise Median Filter Average Filter Wiener Filter Gaussian Filter Motion Filter (Blurring) Cropping Scaling Rotation

Parameter

QF = 70 M=0, V = 0.001 V = 0.04 D = 0.02 3×3 3×3 3×3 3×3 Len=9,Theta=0 25% 50% 5◦

Based on NC

Table 11 Robustness comparison after 1024 bits watermark embedding into Lena with the size of 256 × 256. Attack type

proposed

[21]

45.505 0.9941 1 1 0.9981 1 1 0.4285 0.9372 0.9391 0.9646 1 0.9882 0.8219 0.9489 0.8858

45.5017 0.9523 0.9947 0.9978 0.9969 0.9947 0.9111 0.9586 0.9743 0.9671 0.9984 0.9971 0.8867 0.9675 0.9969 0.7133

can compare the imperceptibility and the robustness of the two methods. Here, the proposed watermarking method is compared with existing watermarking schemes. In these comparisons, different attacks are performed on Lena watermarked image. Then watermarks with various lengths are embedded into Lena host image. Results of these comparisons are given in Tables 10–12. At first, the proposed method is compared with [21] in Table 10. In this case, we determine the Alpha equal to 16.27, so PSNR of our method and [21] are almost the same. Also regarding robustness, our proposed method has higher resistance in Histogram Equalization, Sharpening, JPEG compression and Noises (except Salt & Pepper), but scheme [21] has better performance in filtering (except Gaussian and Motion Filters) and geometric attacks (except Rotation 5◦ ). In the second stage, we embedded a 1024 bits (32 × 32) watermark in Lena host image with the size of 256 × 256 and then we compared the proposed method with [10]. This time, we determine the factor Alpha equal to 36.5. As can be seen in Table 11,

PSNR (dB) Gaussian Noise Blurring Median Filter Wiener Filter JPEG Compression

Cropping Rotation Scaling

Parameter

M=0, V = 0.05 3×3 3×3 QF = 10 QF = 25 QF = 50 QF = 75 QF = 90 25% 50% 180◦ 50%

Based on NC proposed

[10]

37.36 0.5791 0.9843 0.9271 0.9547 0.8652 0.9941 1 1 1 0.7954 0.6153 1 0.9665

37.63 0.942 0.9442 0.9013 0.9340 0.8455 0.9914 1 1 1 0.4156 0.3278 0.9340 1

the PSNR of the two methods are equal. In terms of robustness, the proposed method in all attacks (except Gaussian Noise and Scaling 50%) has higher resistance than [10]. In the third stage, watermark length is enlarged to 4096 bits (64 × 64) and then watermarking performance of the proposed method is compared with [16]. This time, we determine the factor Alpha equal to 37.8, so the achieved PSNR is 33.50 dB, while in the method [16] is 33.1291 dB. Table 12 shows the results of these watermarking methods. It can be observed that the performance of the proposed method in terms of imperceptibility and robustness exceeds the method presented in [16] except Median filter (5 × 5), Scaling 50% and Uniform Noise (Scale =0.005). 6. Conclusion This paper presents a DCT-based image watermarking method that uses the relationship between frequency coefficients to insert the watermark. The above mentioned relationship is stable against possible changes. Also target blocks are selected based on the amount of complexity of the blocks that it can increase the robustness of the proposed method. As well as, a color space called

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Table 12 Robustness comparison after 4096 bits embedding into Lena with the size of 512 × 512. Attack type

PSNR (dB) Gaussian Filter Average Filter Median Filter Scaling Sharpening Gaussian Noise Salt & Pepper Noise Speckle Noise Motion Filter (Blurring) Cropping Uniform Noise JPEG Compression

Parameter

5×5 3×3 5×5 50% M=0, V = 0.01 D = 0.05 V = 0.04 Len=3,Theta=3 20 pixels each side Scale =0.005 QF = 50

Based on NC Proposed

[16]

33.5 1 0.9792 0.8251 0.9733 1 0.9714 0.9523 1 1 0.7795 0.9004 1

33.1291 0.9895 0.9157 0.8545 0.9878 0.9356 0.8272 0.7256 0.8132 0.9678 0.7155 0.9247 0.988

JPEG-YCbCr is used, that its component have a good decorrelation and it has been designed to gain more resistance against most of the attacks in the JPEG image format. The proposed method regards the watermarking problem as an optimization issue. Concerning the watermark embedding method, watermarking parameters and embedding position are the important factors that affect the performance of a watermarking system. In the proposed scheme, a TLBO algorithm is used to automatically determine these values. The TLBO has been inspired by teaching-learning process in a classroom. According to the results, it can be observed that the TLBO makes the implementation of watermarking algorithms easier without the difficulty of determining the watermarking parameters. Besides, the TLBO-based watermarking method provides flexibility in choosing the watermarking parameters according to the need of having robustness against some particular attacks. Compared to other watermarking algorithms, the proposed watermarking method has greater robustness and easier implementation. Supplementary material Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jisa.2019.04.001. References [1] Van Schyndel RG, Tirkel AZ, Osborne CF. A digital watermark. In: Image processing, 1994. Proceedings. ICIP-94., IEEE international conference, 2. IEEE; 1994. p. 86–90. doi:10.1109/ICIP.1994.413536. [2] Moosazadeh M, Ekbatanifard G. Robust image watermarking algorithm using dct coefficients relation in ycocg-r color space. In: Information and Knowledge Technology (IKT), 2016 eighth international conference on. IEEE; 2016. p. 263– 7. doi:10.1109/IKT.2016.7777788. [3] Rao RV. Teaching-learning-based optimization algorithm. In: Teaching learning based optimization algorithm. Springer; 2016. p. 9–39. doi:10.1007/ 978- 3- 319- 22732- 0. [4] Rao RV, Savsani VJ, Vakharia D. Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. ComputAided Des 2011;43(3):303–15. doi:10.1016/j.cad.2010.12.015. [5] Rao RV, Savsani VJ, Vakharia D. Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems. Inf Sci 2012;183(1):1–15. doi:10.1016/j.ins.2011.08.006.

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