An adaptive color image watermarking using RDWT-SVD and artificial bee colony based quality metric strength factor optimization

Applied Soft Computing Journal 84 (2019) 105696

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An adaptive color image watermarking using RDWT-SVD and artificial bee colony based quality metric strength factor optimization ∗

Sourabh Sharma , Harish Sharma, Janki Ballabh Sharma Rajasthan Technical University, Kota, Rajasthan, India

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Article history: Received 10 October 2018 Received in revised form 4 August 2019 Accepted 7 August 2019 Available online 19 August 2019 Keywords: Color image watermarking Redundant discrete wavelet transform (RDWT) Singular value decomposition (SVD) Artificial bee colony (ABC) False positive error (FPE)

a b s t r a c t Image watermarking has emerged as a useful method for solving security issues like authenticity, copyright protection and rightful ownership of digital data. Existing watermarking schemes use either a binary or grayscale image as a watermark. This paper proposes a new robust and adaptive watermarking scheme in which both the host and watermark are the color images of the same size and dimension. The security of the proposed watermarking scheme is enhanced by scrambling both color host and watermark images using Arnold chaotic map. The host image is decomposed by redundant discrete wavelet transform (RDWT) into four sub-bands of the same dimension, and then approximate sub-band undergoes singular value decomposition (SVD) to obtain the principal component (PC). The scrambled watermark is then directly inserted into a principal component of scrambled host image, using an artificial bee colony optimized adaptive multi-scaling factor, obtained by considering both the host and watermark image perceptual quality to overcome the tradeoff between imperceptibility and robustness of the watermarked image. The hybridization of RDWT-SVD provides an advantage of no shift-invariant to achieve higher embedding capacity in the host image and preserving the imperceptibility and robustness by exploiting SVD properties. To measure the imperceptibility and robustness of the proposed scheme, both qualitative and quantitative evaluation parameters like peak signal to noise ratio (PSNR), structural similarity index metric (SSIM) and normalized cross-correlation (NC) are used. Experiments are performed against several image processing attacks and the results are analyzed and compared with other related existing watermarking schemes which clearly depict the usefulness of the proposed scheme. At the same time, the proposed scheme overcomes the major security problem of false positive error (FPE) that mostly occurs in existing SVD based watermarking schemes. © 2019 Elsevier B.V. All rights reserved.

1. Introduction The emergence of the digital era because of the rapid development of technology and the internet allows accessing, copying, manipulating and sharing digital data among people very easily. Watermarking is the most famous and commonly adopted solution for digital right management due to its effectiveness against security threats. Digital watermarking is a technique of embedding secret information known as a watermark (text, logo, image, etc.) into the host image, which can be extracted later to prove the authenticity and ownership of a legitimate user [1– 5]. Color images are playing a vital role in multimedia driven society; therefore, researchers are now more focused on color image watermarking schemes. Since the majority of the watermarking schemes reported in the literature are gray image ∗ Correspondence to: Department of Computer Science, Rajasthan Technical University, Kota, Rajasthan, India E-mail addresses: [email protected] (S. Sharma), [email protected] (H. Sharma), [email protected] (J.B. Sharma). https://doi.org/10.1016/j.asoc.2019.105696 1568-4946/© 2019 Elsevier B.V. All rights reserved.

schemes which cannot be directly extended for color images due to higher embedding capacity and the visual transparency of the color image which depend on both brightness and chrominance information [6–8]. In any watermarking scheme, imperceptibility, robustness (i.e. the capability to resist the signal processing attacks), and watermarking capacity are the major concerns because watermarking should not be done at the ease of altering the visibility of the host image while improving the other performance parameters. So the selection of the secret embedding key is very crucial while doing the watermarking to overcome the above-said tradeoff [6,9]. In order to increase the robustness of a watermarked image, robust watermarking schemes modify the pixels of the host image. Similarly, watermarking capacity is improved using higher values of the embedding coefficient. These modifications may cause serious distortions and degrade the visual quality of the watermarked image and hacker/attacker may detect and decode the embedded watermark. Therefore, the development of a watermarking scheme having high robustness, higher capacity and resistant to

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false detection with good perceptual quality is still a significant issue among the researchers. However, to satisfy the above constraint watermarking scheme using different transforms such as DFT [9], DCT [10,11], FrFT [7, 12,13], DWT [14,15], RDWT [16–19], etc. have been developed. Moreover, hybrid schemes combining these transform with singular value decomposition (SVD) have become very popular during the past few years [20–23]. In SVD based schemes embedding in S (singular values) component is preferred because of its capability to resist attacks, despite applying major variation in this matrix, while embedding a watermark in the S matrix of host image suffers a security problem of false positive error [24,25]. Different improved robust watermarking schemes using SVD, but free from false positive error have also been reported [6,26,27]. In the recent past years, a big challenge is to design a watermarking scheme which embeds a color watermark into a color image of the same size and dimensions. Mostly watermarking schemes in the literature are designed for grayscale images [18, 28,29]. Color image watermarking techniques reported in the literature have used either a binary, a gray or pseudo-random sequence as a watermark, for example, Roy.et al. in [17] presents an SVD based location specific in RDWT domain using YCbCr color space. An Arnold scrambled grayscale logo is used to embed in the Y component of host image blockwise. Su.et al. [30] proposed a color image watermarking in YCbCr color space. DC coefficients have been calculated using color quantization for each non-overlapping blocks of the Y component to embed a scrambled binary watermark. Similarly, a binary image is embedded into a color host image in the hybrid DCT and DWT [31]. An adaptive pixel-wise embedding strength is generated using hamming codes of the host image. Encryption of binary watermark is done before embedding into selected row and column of host image making the watermarking robust against attacks. DWT-SVD domain watermarking is presented in [32] and [19]. In these schemes, watermark information is embedded in the singular values of Y and Cb components respectively, with a fixed value of embedding strength factor. In order to enhance security and visual transparency QR code and of watermark has been applied in [32], while Arnold scrambling and adaptive scaling factor calculated by just noticeable distance (JND) of the host has been used in [19]. A color image adaptive watermarking using Quaternion discrete fractional random transform is proposed in which a binary watermark is embedded in the color host image [33]. Very few color watermarking schemes used color watermark but of smaller size and dimension as compared to host image due to degradation of perceptual quality. In [26,34] R, G, and B channels of 32 x 32 size watermarks have been embedded in singular values of 512 x 512 sized color host image. While in [35] Su.et al. proposed an LU factorization of each R, G and B component of 512 x 512 size host image using second and thirdrow elements of the first column to embed a 32 x 32 size color watermark. In watermarking schemes, selection of embedding scaling factor plays a crucial role to balance the imperceptibility and robustness. Most of the schemes use a single constant scaling factor which works on the selected images [17,18]. But if the input images are changed then it will not provide efficient results. On the other hand, an image adaptive selection of strength factor will overcome this dependency. In [36] a hybrid DWT-DCT is proposed which use Quantization Index Modulation (QIM) to generate the quality metric based adaptive embedding strength factor. Furthermore, optimization is emerged as a useful method to calculate the optimal scaling factor instead of random selection. In [37] Markov Chain Monte Carlo (MCMC) is introduced for

optimizing complex stochastic statistical inference. MCMC sampling and optimization for high dimensional multimodal distribution is slow in convergence rate or provide a biased sampling. In [38] BFGS (Broyden–Fletcher–Gildfarb–Shanno) optimization is applied on the Warmhole Hamiltonian Monte Carlo (WHMC) to maximize the search space and incorporate the convergence rate and sampling. In [39] population based orthogonal-MCMC is implemented to advance the exploration of state space for the higher dimensional computation. By using parallel simulated annealing along with orthogonal-MCMC, improve the robustness and efficiency of optimization. Various optimization algorithms which are inspired by nature have also been used in watermarking recently to optimize the tradeoff between imperceptibility and robustness by computing dynamic scaling factor [1–4,21]. Gray-scale image watermarking using DWT-SVD with firefly algorithm and DWT-SVD with ABC have been reported in [3] and [27] respectively. The multi-scaling factor used for embedding is optimized by the firefly and ABC algorithms for better imperceptibility and robustness. In [27] the principal components of Arnold scrambled watermark image is used to embed in the singular values of the host image, which makes this scheme free from false positive error problem. A color watermark is used to embed into the color host images in DWTSVD domain with an optimal scaling factor which is derived using the ABC optimization algorithm [20]. Self-adaptive differential evaluation optimization is used to generate embedding factor to embed a 512 x 512 watermark image into the 512 x 512 grayscale host image in RDWT-SVD domain [21]. An 8-bit digital signature is also embedded to solve the problem of false positive error. In [40] RIDWT (Redistributed invariant wavelet transform) along with SVD are used to embed a binary watermark into a grayscale host image. ABC optimization is used to optimize the scaling parameters. A non-blind robust watermarking is described in [23] by employing the IWT (integer wavelet transform), SVD and ABC for embedding a grayscale watermark into a grayscale host image. Signature verification using user-supplied orthogonal matrices at the time of extraction is done to overcome the false positive error. From the discussions of the above literature, it may be noted here that, RDWT provides higher embedding capacity, while adaptive and multiple scaling factors provide improved security, robustness, and visual transparency performance. None of the adaptive color watermarking schemes, embedding color watermark which is of the same size of host image with a dynamic scaling factor and free from false positive error using RDWTSVD and ABC, has been reported. Therefore, in this paper, a color watermarking scheme using RDWT-SVD and ABC is presented. RDWT is redundant and shift-invariant and its decomposed components are of the size of host image hence provides improved watermark embedding capacity, while the use of ABC provides a dynamic and adaptive scaling factor. ABC optimization is preferred in the proposed scheme as it is simple, requiring fewer control parameters hence the low computation cost as compared to other optimization algorithms [41]. The proposed scheme is free from false positive error because the watermark is embedded in principal components of the host rather than in singular values. The robustness of the proposed scheme is also tested by applying multiple attacks on the watermarked image and correctly extracts the watermark information. The main contributions of the proposed work are listed as:

• Embedded a color watermark over the same dimension color host image using shift-invariant RDWT, to signify higher embedding capacity. • Generating a robust watermarking by dynamic image adaptive (both host and watermark), ABC optimized multi-scaling embedding strength factor by exploiting quality metric strength factor.

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Fig. 1. Description of 1-D RDWT analysis and synthesis.

Fig. 2. Original color test images (a) Lena, (b) Plane, (c) House, and (d) Peppers.

• Remove the security threat of false positive error, for providing rightful ownership and copyright protection to the legitimate users • Overcome the tradeoff between imperceptibility and robustness even by applying multiple attacks and correctly extract the embedded watermark from the watermarked image. The rest of this paper is organized as: Section 2 presents the basic preliminaries required in the watermarking scheme. The proposed algorithm is explained in Section 3; While Section 4 presents the optimization of the embedding strength factor using ABC. The experimental results and discussions are presented in Section 5. Finally, the conclusion of the proposed work is presented in Section 6.

Fig. 3. Color watermark images (a) Peugeot logo (b) 8Color image (c) RTU logo.

unauthorized access of watermark even after successful extraction. Arnold chaotic map for a square image of dimension Nx N is described in Eq. (3).

[ ′] x y′

2. Preliminaries 2.1. YCbCr color space Information redundancy between three components of RGB image is relatively close to each other which alter the perceptibility of the host image once a watermark is embedded in it [42]. Hence, firstly an RGB color image is converted into a YCbCr color image. The modification in Y (luminance) component is used for doing watermarking because it is less susceptible to HVS (Human visual system). Conversion from RGB color space to YCbCr color space and vice-versa can be done by using the described relation in Eq. (1) and Eq. (2) as follows.

{ +1.000 = +1.000 +1.000 { } { Y +0.299 Cb = −0.169 Cr +0.500 R G B

{ }

−0.000 −0.344 +1.773 +0.587 −0.311 −0.419

}{ } +1.403 Y −0.714 Cb − 128 +0.000 Cr − 128 }{ } { } +0.114 R 0 +0.500 G + 128 −0.081 B 128

1 = 1

][ ]

1 2

x mod N y

(3)

Where (x, y) is the pixel value of the original component of the image, while (x′ , y′ ) is the pixel value of the scrambled image after the ith iteration. Let P represents the period of an image and can be defined as the number of iterations after which the pixels at position (x, y) say to obtain its original shape. Size of the input image determine the period P, if an image is iterated T-times then it can be obtained back by transforming it by (P − T ) times. Here T as number of iterations can be used as Arnold chaotic map key Ka . To unscramble the scrambled image, the inverse-Arnold chaotic map is applied using the Eq. (4).

[ ] (1)

[

x = y

([

2 −1

][ ] −1 x′ 1

y′

[ ]) +

N N

mod N

(4)

2.3. RDWT (2)

2.2. Arnold chaotic map To provide the rightful ownership and extra security while embedding and extracting the watermark, Arnold chaotic map is used in the processing of the algorithm. Arnold chaotic map is an iterative scrambling, which jumbles the pixel position of the image to generate a new chaotic image. Hence it prevents the

The wavelet transforms especially discrete wavelet transform (DWT) is very common and is used in the various fields of signal processing, including digital watermarking because of its decomposition in both spatial and frequency resolution. The low pass and high pass filter and downsampling feature of DWT, analyze an image and decompose it into multiresolution analysis i.e. approximation, horizontal, vertical and diagonal details of the image. But because of the downsampling of the image, it does not provide shift invariance. This originates major change

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Fig. 4. Proposed embedding scheme block diagram. Table 1 ABC optimization control parameters. ABC Parameters

Values

Number of Swarms Maximum Number of cycles for foraging Limit Optimization Parameter [PSNR] Number of Onlooker bees Number of Employed bees Number of Scout bees Fitness Function parameters

20 20 25 [1 50] 50% of Number of Swarms 50% of Number of Swarms Changeable Additive noise, Filtering attacks, Geometric attacks

[LB UB]

Attacks

in the wavelet coefficient of the image even for minor modifications and the watermark will not be extracted properly from the host image. To overcome this shortcoming of sifting variant, a new wavelet RDWT is generated whose resolutions are shift invariant in its property. RDWT decomposes the image into four sub-bands LL, LH, HL and HH with the same dimension as of original image [16]. This will increase the embedding capacity of a watermark into the host image. In Fig. 1 analysis and synthesis of an RDWT is explained as: (i) RDWT Analysis: km [n] = (km+1 [n] ∗ hm [−n])

(5)

lm [n] = (km+1 [n] ∗ gm [−n])

(6)

(ii) RDWT Synthesis: km+1 [n] =

1 2

(km [n] ∗ hm [n] + lm [n] ∗ gm [n])

(7)

Where g [−n] and h [−n] represent the high pass and low pass analysis filters; while g [n] and h [n] represent the high pass and low pass synthesis filters respectively. lm and km represent the high band and low band of the coefficient at mth level. 2.4. SVD SVD is a factorization method used in linear algebra, which decomposes a symmetric matrix into three matrices U (unitary

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Fig. 5. Proposed extraction scheme block diagram. Table 2 The results for imperceptibility (PSNR and SSIM) between the original host image and watermarked image and robustness (NC) between the watermark and extracted watermark for the proposed watermarking scheme under no attack. Host image

Watermark

PSNR

SSIM

Lena

Peugeot logo 8Color image RTU logo

77.4875 60.1620 59.6441

0.9940 0.9998 0.9998

Plane

Peugeot logo 8Color image RTU logo

73.1904 59.3682 59.9021

0.9834 0.9970 0.9971

House

Peugeot logo 8Color image RTU logo

73.6863 63.9114 59.8773

0.9940 0.9991 0.9995

Pepper

Peugeot logo 8Color image RTU logo

72.3545 67.4618 59.9565

0.9951 0.9995 0.9998

matrix), S (diagonal matrix) and V (complex unitary matrix). Let A be a symmetric matrix of dimension m x m its SVD can be calculated as. SVD(A) = [USV T ]

(8)

The Principal Component (PC ) of a matrix can be obtained by multiplying the U and S matrices as shown by Eq. (9). Principal component encloses the unique feature of any matrix and hence can be utilized in this work to embed the watermark to overcome the problem of false positive. PC = U ∗ S

(9)

2.5. ABC optimization ABC optimization is an evolutionary algorithm based on population size is presented by Karaboga in the year 2005 [43]. This optimization is stimulated by the actions of honey bees present in nature. ABC optimization is quite popular in different problems [27,41,44] to find out the optimal value of the variable in the given search space in order to minimize or maximize a given cost function. In ABC the bees are characterized into three groups viz. Employed bees, onlooker bees, and scout bees.ABC operates on repetitive iteration to find the optimal solution: 1. Initialization Phase: In the initialization process, the population of N size solution is randomly selected, where each solution xi (i = 1, 2, . . . , N) is a D-dimensional vector. Here D is the number of the variable used in the optimization and xi represents ith food source described as in Eq. (10). xi,j = xmin,j + rand(0, 1)(xmax,j − xmin,j )

(10)

Where xi,j bounds between xmin,j and xmax,j in jth direction, while rand(0, 1) is a random variable whose value lies between 0 and 1. The fitness value is obtained after running all N solution. 2. Employed bees phase: The employed bees continuously update the solution (food source) by gaining information from the fitness value of the new solution along with individual experience. Employed bees keep the updated fitness value while discarding the older ones with the Eq. (11). x′i,j = xi,j + Φi,j (xi,j − xk,j )

(11)

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Fig. 6. The Optimization process of the proposed watermarking scheme.

Fig. 7. The watermarked images (a) Lena, (b) Plane, (c) House, (d) Pepper and extracted watermark Peugeot logo.

Fig. 8. The watermarked images (a) Lena, (b) Plane, (c) House, (d) Pepper and extracted watermark 8Color image.

Fig. 9. The watermarked images (a) Lena, (b) Plane, (c) House, (d) Pepper and extracted watermark RTU logo.

Fig. 10. The watermarked image (Lena) and extracted watermark (Peugeot logo) after applying additive noise attack (a) Salt & pepper noise (0.02), (b) Gaussian noise (0.1, 0.5) and (c) Speckle noise (0.5).

Fig. 11. The watermarked image (Lena) and extracted watermark (Peugeot logo) after applying filtering attacks (a) Median filter (5, 5), (b) Wiener filter (3, 3) and (c) Butterworth lowpass filter (100, 1).

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Fig. 12. The watermarked image (Lena) and extracted watermark (Peugeot logo) after applying geometrical attacks (a) Scaling (1/2), (b) Cropping (50%) and (c) Rotation (5◦ ).

Fig. 13. The watermarked image (Lena) and extracted watermark (Peugeot logo) after applying attacks (a) Sharpening, (b) Histogram equalization and (c) Gamma correction (0.25).

Fig. 14. The watermarked image (Lena) and extracted watermark (Peugeot logo) after applying attacks (a) Cut (10), (b) Shear (0.1, 0.1) and (c) Translation (20, 20).

Where k ∈ 1, 2, . . . , N and j ∈ 1, 2, . . . , D indices are selected randomly, such that k must be selected other than i. Φi,j is a random number lies in the range [−1, 1]. 3. Onlooker bees phase: The new updated fitness value obtained by employed bees for a new solution (food source) is shared in the hive. Now the onlooker bees choose a solution (food source) using Eq. (12). fitnessi

Probabilityi = ∑N i

(12)

fitnessi

4. Scout bees phase: Abandoned solution (food source) whose position remains same becomes the scout bees and changed by random solution (food source) contained in the population. To decide the abandonment a predefined limit value is used. Suppose xi is the abandoned food source, then scout bees replaced this by randomly chosen food source using Eq. (10). 5. Termination phase: ABC has three main control parameters viz. number of solutions (food source) i.e. a number of an onlooker or employed bees, limit value and the maximum number of iterations. ABC performs above steps 2, 3 and 4 iteratively until the best optimal solution is obtained. 2.6. Evaluation parameters

[

3 ∗ Max2i MSER + MSEG + MSEB

MSE =

] (13)

3 m n ∑ ∑ ∑

1 3m ∗ n

[I (i, j, c ) − M(i, j, c)]2

(14)

c =1 i=1 j=1

Where m, n represents the dimension of the image, while c is the color component. Another parameter to measure the imperceptibility is SSIM (structural similarity), which describes the structural similarity measurement between the original image and the watermarked image. The value of SSIM near to 1 represents that the structure of the two images are identical by considering the three aspects of the human visual system i.e. luminance distortion (ld), contrast distortion (cd) and loss of correlation (lc) can be calculated as: SSIM = ld (IRGB , MRGB ) cd (IRGB , MRGB ) lc(IRGB , MRGB )

(15)

The robustness of the watermarking represents the degree of similarity between the watermark image W (i, j) and extracted watermark image E(i, j). Normalized cross-correlation (NC) is used to measure the robustness, whose value generally lies in the range [−1, 1]. The NC value near to 1 represents the robust nature of the watermarking scheme, it can be calculated as: NC =

To measure the imperceptibility, alteration in the perceptual quality between the original host and watermarked is computed with the help of quality matrices PSNR and SSIM [26]. In an invisible watermarking scheme watermark should be invisible according to HVS properties. PSNR for the color image can be computed as: PSNR = 10 log10

Where Maxi the maximum pixel value of an image, MSER , MSEG , MSEB are the mean square error between the watermarked image M(i, j) and host image I (i, j) of R, G and B component of an image respectively. MSE can be calculated as:

3 ∑

∑m ∑n i=1

j=1 (W

∑m ∑n c =1

i=1

(i, j, c ) ∗E(i, j, c))

j=1 (W

(i, j, c ))2

(16)

2.7. Adaptive embedding strength factor generation The generation of the embedding strength factor is a very crucial task because it decides important features such as imperceptibility of the watermarked image and robustness. Therefore in order to keep a check over the imperceptibility and robustness of the watermarked image, strength factor is generated from the

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Fig. 15. The watermarked image (Lena) and extracted watermark (RTU logo) after applying additive noise attack (a) Salt & pepper noise (0.02), (b) Gaussian noise (0.1, 0.5) and (c) Speckle noise (0.5).

Fig. 16. The watermarked image (Lena) and extracted watermark (RTU logo) after applying filtering attacks (a) Median filter (5, 5), (b) Wiener filter (3, 3) and (c) Butterworth lowpass filter (100, 1).

Fig. 17. The watermarked image (Lena) and extracted watermark (RTU logo) after applying geometrical attacks (a) Scaling (1/2), (b) Cropping (50%) and (c) Rotation (5◦ ).

Fig. 18. The watermarked image (Lena) and extracted watermark (RTU logo) after applying attacks (a) Sharpening, (b) Histogram equalization and (c) Gamma correction (0.25).

Fig. 19. The watermarked image (Lena) and extracted watermark (RTU logo) after applying attacks (a) Cut (10), (b) Shear (0.1, 0.1) and (c) Translation (20, 20).

parameter that describes the perceptual capability of the watermarked image i.e. PSNR (peak signal to noise ratio). The proposed algorithm is made adaptive according to the type of input images (both host and watermark) which are used for watermarking. For example, the host image is I(i, j), watermark image is W (i, j) and the watermarked image is M (i, j). The pixel by pixel PSNR is calculated as.

[ PSNR = 10 log10

2552 E (i, j)

(19)

Where sf is the scaling factor. I (i, j) − M (i, j) = −sf ∗ W (i, j) 2552

[

(17)

2

[

M (i, j) = I (i, j) + sf ∗ W (i, j)

PSNR = 10 log10

]

(20)

]

(−sf ∗ W (i, j))2

(21)

On the basis of Eq. (21), we find the value of sfx where

where E (i, j)2 is the pixel wise mean square error between host and watermarked image. PSNR = 10 log10

Since,

2552

]

(I (i, j) − M (i, j))

2

(18)

{xϵ 1 and 2} for both the host and watermark image. ⎡ ⎤ sf1 (i, j) = ⎣

255 I (i, j) ∗ 10

(

PSNR 20

)⎦

(22)

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Fig. 20. Graphical results of NC of the extracted watermark (Peugeot logo) from different host images under various attacks (a) Salt & pepper noise, (b) Gaussian noise, (c) Speckle noise, (d) Median filtering, (e) Average filtering, (f) Weiner filtering, (g) JPEG compression, and (h) JPEG2000 compression.

⎡ sf2 (i, j) = ⎣

⎤ 255 (

W (i, j) ∗ 10

PSNR 20

)⎦

Now with the help of Eqs. (22) and (23), an image adaptive (23) embedding strength factor α is generated which decides the

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Fig. 21. Results of multiple attacks taking watermarked image (Lena) and extracted image (Peugeot logo) under (a) Salt & pepper noise (density=0.2) + JPEG compression (Q=30), (b) Histogram Equalization + Rotation (clockwise 5◦ ), (c) Gaussian noise (M=0.1,var=0.2) + Cropping (25%), (d) Sharpening + Average filter (5, 5), (e) Speckle noise (var=0.2) + Shear (x=0.1, y=0.1), (f) Wiener filter (5, 5) + Translation (10, 10), (g) Gaussian filter (5, 5) + Cut (10 columns), (h) Gamma correction (gamma=0.25) + JPEG compression (Q=50), (i) Average filter (5, 5) + Rotation (anticlockwise 5◦ ), (j) JPEG compression (Q=50) + Shear (x=1, y=0.2), (k) Salt & pepper noise (density=0.2) + Gaussian noise (M=0.1,var=0.2) (l) Histogram equalization + Gamma correction (gamma=0.25).

Fig. 22. Results of multiple attacks taking watermarked image (Lena) and extracted image (RTU logo) under (a) Salt & pepper noise (density=0.2) + JPEG compression (Q=30), (b) Histogram Equalization + Rotation (clockwise 5◦ ), (c) Gaussian noise (M=0.1,var=0.2) + Cropping (25%), (d) Sharpening + Average filter (5, 5), (e) Speckle noise (var=0.2) + Shear (x=0.1, y=0.1), (f) Wiener filter (5, 5) + Translation (10, 10), (g) Gaussian filter (5, 5) + Cut (10 columns), (h) Gamma correction (gamma=0.25) + JPEG compression (Q=50), (i) Average filter (5, 5) + Rotation (anticlockwise 5◦ ), (j) JPEG compression (Q=50) + Shear (x=1, y=0.2), (k) Salt & pepper noise (density=0.2) + Gaussian noise (M=0.1,var=0.2) (l) Histogram equalization + Gamma correction (gamma=0.25).

3. The proposed scheme

strength of the watermark.

∝=

[sf1 (i, j) + sf2 (i, j)] 2

[

] 255 (

∝=

I (i,j)∗10

PSNR 20

)

[ +

] 255( W (i,j)∗10

2

PSNR 20

)

(24)

Hence the value of scaling factor ∝ is adaptive to the pixelby-pixel value of host and watermark and depends on the value of PSNR as derived in Eq. (24).

In most of the color watermarking schemes, the watermark used for embedding are both binary or grayscale images and the dimensions of watermark used for embedding are smaller than the dimensions of the host image [15,24,35]. In this paper, a new watermarking scheme is proposed in which both the host and watermark images are color images and dimensions of the watermark are equal to the size of the host image. Both the host and watermark images are preprocessed from RGB color space to YCbCr color space for further processing. The Y luminance components are used for the embedding process. To provide extra security while embedding both the host and watermark images

Host image

Makbool et al. [16]

Roy et al[17]

Su et al. [45]

Abdelha kim et al. [1]

Patvardhan et al. [32]

Kalra et al. [31]

Gupta et al. [20]

Su et al. [35]

Abdelhakim et al. [22]

Ali et al. [40]

Ansari et al. [23]

Proposed Scheme

Lena

55.0678/ 0.9988

50.45/ –

49.9898/ 0.9872

53.94/ –

54.9980/ –

42.0109/ 0.9787

35.92/ 0.98

39.4428/ 0.9816

50.73/ –

44.0207/ –

45.1242/ –

77.4875/ 0.9940

Plane

55.7450/ 0.9991

–

49.8664/ 0.9854

–

54.2865/ –

39.7644/ 0.9851

–

36.6958/ 0.9675

–

–

44.9321/ –

73.1904/ 0.9834

House

52.9223/ 0.9971

–

–

57.89/ –

–

38.1103/ 0.9854

–

40.1223/ 0.9793

–

–

44.7322/ –

73.6863/ 0.9940

Pepper

55.1221/ 0.9989

50.62/ –

50.0839/ 0.9859

54.60/ –

–

42.6843/ 0.9816

35.23/ 0.96

40.8216/ 0.9878

52.53/ –

43.0222/ –

44.9243/ –

72.3545/ 0.9951

S. Sharma, H. Sharma and J.B. Sharma / Applied Soft Computing Journal 84 (2019) 105696

Table 3 Comparative analysis of the proposed algorithm with various other watermarking schemes for imperceptibility using PSNR (dB) and SSIM.

11

12

S. Sharma, H. Sharma and J.B. Sharma / Applied Soft Computing Journal 84 (2019) 105696

are encrypted using Arnold chaotic map. The principal components of the host image are modified by the scrambled watermark using an image adaptive optimized scaling factor. To extract the watermark, firstly the watermarked image is converted to YCbCr color space. The Y luminance component is selected and scrambled by Arnold chaotic map. Thereafter scrambled luminance component is decomposed by RDWT to obtain watermarked LL component, and SVD is applied to generate the extracted principal component. The embedded luminance component of the watermark is computed and merge with other components. Finally, the inverse Arnold chaotic map is applied and YCbCr color space is transformed to RGB color space to obtain the extracted watermark. This makes the proposed scheme more secure and authentic than other methods and makes a better choice for rightful ownership and copyright protection applications. In this section, the embedding and extraction steps of the proposed scheme are described as: 3.1. Embedding scheme The block diagram of the embedding scheme is shown in Fig. 4. The proposed embedding scheme is explained into three phases viz. (a) Pre-processing Phase, (b) Embedding Phase and (c) Post-processing Phase as shown below. (a) Pre-processing Phase: Step 1 Suppose I represents original host color image of dimension 512 x 512. Firstly, I is transformed into a YCbCr color space, which leads to components IY , ICb , ICr . Select IY (luminance) component and scramble it by Arnold chaotic map to obtain I˜ Y. Step 2 Suppose W represents the color watermark of dimension 512 x 512, firstly scramble W by Arnold chaotic map to provide extra security level. Transform scrambled watermark to ˜ ˜ ˜ YCbCr color space to obtain W Y ,WCb , WCr components. (b) Embedding Phase: Step 3: Apply single level Haar redundant wavelet transform I I I on the I˜ Y of the host image to decompose it into LLY , LHY , HLY I I and HHY sub-bands. Select the LLY subband and perform singular T matrices. value decomposition to obtain the ULL , SLL and VLL T SVD LLIY = ULL SLL VLL

(

)

[

]

(25)

Step 4: Generate the image adaptive embedding strength factor α as explained in Section 2.7 by taking the watermark com˜Y and the host image component LLIY obtained in step ponent W 2 and step 3 respectively.

[

] 255 (

PSNR LLIY ∗10 20

)

[

] 255 (

+

PSNR 20 ˜ W Y ∗10

IPC = ULL ∗ SLL

(27)

Step 6: Modify the principal component IPC using image adap˜Y component of the wative strength factor by the scrambled W termark. And then again compute the singular value decomposi′ tion of IPC . ′ ˜Y IPC = IPC + α ∗ W

(28)

Where α is the scaling factor generated in Eq. (31) and ∗ represents the pixel-wise dot product.

[

′T

′ ′ ′ SVD IPC = ULL SLL VLL

(

)

′T

]

T ′ VLL LLIW = ISVD ULL SLL

(

)

(30)

(c) Post-processing phase: Step 8: Apply the inverse Arnold chaotic map on IY′ component to obtain unscrambled watermarked luminance component IYN . Step 9: Merge the watermarked IYN (luminance) component with components ICb and ICr obtained in step 1, and convert from YCbCr to RGB color space to obtain the watermarked image M. 3.2. Extraction scheme Extraction is the inverse process of embedding, in which the embedded watermark is extracted by using the information, known only to the legitimate user which are obtained while ′T ′ embedding, i.e. side information(ULL , VLL ), strength factor α and ˜ ˜ scrambled components W Cb and WCr . The block diagram of the proposed extraction scheme is shown in Fig. 5. The extraction process is also divided into three phases viz. (a) Pre-processing phase, (b) Extraction phase and (c) Post-processing phase is explained step-by-step below. (a) Pre-processing Phase: Step 1: Firstly the modified\attacked color watermarked image M is transformed from RGB to YCbCr color space to obtain MY , MCb , MCr components. Step 2: Select the MY (Luminance) component of watermarked and scramble it by Arnold chaotic map, to obtain scrambled ˜Y . watermarked luminance component M (b) Extraction Phase: Step 3: Apply single level Haar redundant wavelet transform M M M ˜Y to decompose it into LLM on M Y , LHY , HLY and HHY sub-bands. sub-band and perform singular value decomposiSelect the LLM Y M M M tion to obtain the ULL , SLL and VLL matrices. Y

SVD

LLM Y

(

)

=

[

T M M ULL S VM Y LLY LLY

Y

Y

]

(31)

Step 4: Compute the extracted principal component EPC using M ′ , V ′ TLL SLL generated in step 3 above and side information (ULL Y matrices) obtained while embedding. T

′ M EPC = ULL ∗ SLL ∗ V ′ LL Y

(32)

Step 5: Compute the extracted watermark luminance component EY′ using the strength factor ∝.

)

(26) 2 Step 5: Calculate the principal component IPC of the host image using the left orthogonal ULL and singular value SLL matrices.

∝=

Step 7: Perform inverse SVD (ISVD) to obtain modified LLIW and then apply single level Haar inverse redundant wavelet transform on modified LLIW sub-band with other three sub-bands to create watermarked luminance component IY′ .

(29)

′ Keep ULL , VLL matrices as side information, for extracting of the watermark.

EY′ = (EPC − IPC ) /∝

(33)

(c) Post-processing Phase: Step 6: The extracted EY′ (luminance) component with is ˜ ˜ merged other scrambled W Cb , WCr components which are obtained during the embedding process and then inverse Arnold chaotic map is applied to them. Finally, YCbCr color space is transformed to RGB color space to obtain the color extracted watermark imageE. 4. Optimization of scaling factor using ABC The robustness and imperceptibility of any watermarking scheme depends upon the selection of embedding strength factor. Mostly watermarking schemes select a constant value as a strength factor for embedding and extracting the watermark. The proposed scheme uses the image adaptive embedding strength factor using the qualitative measurement parameter i.e. PSNR considering both the host and watermark image, which is also

S. Sharma, H. Sharma and J.B. Sharma / Applied Soft Computing Journal 84 (2019) 105696

optimized by the ABC algorithm. To calculate the value of embedding strength factorα , N different types of attacks are supposed to be applied to check the robustness during the optimization process. The tradeoff between robustness and imperceptibility of the proposed scheme is minimized by maximizing the proposed objective function(F ). F = SSIM (I , M ) +

N ∑

NC (W , Ei )/N

(34)

i=1

Where I is the color host image and M is the color watermarked image, while W is the original color watermark and E is the extracted color watermark from the attacked watermarked image, while N represents the number of attacks. The block diagram of the ABC optimization process is shown in Fig. 6. The value of control parameters used to implement ABC optimization is shown in Table 1. 5. Experimental results and discussion The experiments of the proposed watermarking scheme are performed on a software tool MATLAB 2014a. In order to evaluate the performance of the proposed scheme four 24-bit color host images of sizes 512 x 512, namely Lena, Plane, House, and Pepper are selected from the database CVG-UGR [46] as shown in Fig. 2. Three color watermark images of the same sizes as host images are used i.e. Peugeot logo, 8Color image [35] and RTU logo which is taken from Rajasthan Technical University Kota, Rajasthan India as shown in Fig. 3. The performance of the proposed watermarking scheme is measured in terms of visibility and robustness. The robustness of the proposed scheme is evaluated by applying image manipulation attacks like additive noise, filtering attacks and geometrical attacks, etc. The ABC parameters used to optimize the image adaptive quality metric strength factor are listed in Table 2. A new objective function is proposed to maximize the imperceptibility (according to HVS) and robustness against attacks. A comparative analysis is also performed with other related watermarking schemes to show the effectiveness of the proposed scheme. 5.1. Imperceptibility results To measure the perceptual quality of the proposed scheme, PSNR and SSIM are computed for all the test color host images taking different watermarks as shown in Table 2. Generally, the PSNR higher than 30 dB and SSIM higher than 0.9 are assumed to be good enough according to the human visual system (HVS), which depicts that the watermark is invisible in the watermarked image. The watermarked image and extracted watermark for all the test host images taking different watermark are shown in Figs. 7–9. According to Table 2, PSNR and SSIM values computed between the original color host and watermarked images are higher than 55 dB and 0.9 respectively. For further viewing the invisibility, we compared the proposed scheme with other related watermarking schemes present in literature which uses optimization algorithms [1,20,22,23,40] and not using optimization algorithms [16,17,31,32,35,45] as shown in Table 3. It is inferred from Table 3 that the imperceptibility results of the proposed scheme outperform the other schemes. 5.2. Robustness results The robustness of the proposed scheme is examined by applying different attacks on the watermarked image and measuring the resemblance between the extracted watermark and original

13

watermark. Normalized cross-correlation (NC) is used to measure the robustness. The attacks are categorized as additive noise attacks, filtering attacks, geometrical attacks, blurring, sharpening, and compression attacks. The visual results of robustness taking Lena as the host image, and Peugeot logo and RTU logo as watermark are shown in Figs. 10–19. It is clearly observed that the proposed watermarking scheme is robust when subjected to different attacks. Table 4 shows NC values of extracted watermark images (Peugeot logo and RTU logo) by applying different attacks with varying parameter for the Lena, Plane and House host images. The impact of any attack may vary with its intensity level. For all the test host images the NC values of different attacks with varying intensity level taking the Peugeot logo as watermark are shown graphically in Fig. 20. It is noted from Fig. 20 that NC values are higher for Lena image under salt & pepper noise and speckle noise, and for House image under Gaussian noise. For filtering attacks like median filter, average filter and wiener filter House image has higher results of NC value. The Plane image is having higher NC values for JPEG compression attack and JPEG2000 compression attack. JPEG2000 is the advanced form of compression attack, where a compression ratio like n (or n : 1) represents that the original image has n bits of information for every 1 bit of compressed image. 5.3. Comparative analysis To prove the robust nature of the proposed scheme under different watermarking attacks the results obtained in the present work are compared with other related watermarking schemes as: 5.3.1. Comparison with optimization algorithm based watermarking schemes In [4] genetic algorithm is used to produce an optimal value of embedding factor to embed a grayscale watermark to the grayscale cover image. In [21] same size grayscale watermark is used to embed in the grayscale host image using self-adaptive differential evaluation for optimization of strength factor. While in [22] ABC optimization is used for embedding a binary watermark into a grayscale host image in hybrid FrFT-SVD domain. In all the schemes [2,4,22,23,40] the grayscale image with grayscale or binary watermark of a size smaller than host image have been used; these schemes have also utilized different optimization algorithms. Therefore on the basis of optimization, the proposed algorithm is also compared with above state-of-the-art algorithms for its robustness performance by comparing NC value under different attacks which are depicted in Table 5. From results, it can be easily derived that the proposed scheme is robust against different attacks. 5.3.2. Comparison with color watermarking schemes without optimization algorithms In [17] a binary watermark is used to insert in color host image using predefined scaling factor for embedding and extraction of watermark using singular values which is more prone to a security threat of false positive error, while in the proposed scheme a color watermark is embedded into the principal component of host image which overcomes the false positive error problem. In other schemes [26,34,35,45] the color watermark is used to embed in the color host image which is very similar to the proposed scheme, but because of the adaptive and optimized nature of the proposed scheme, the robustness results are superior. Comparison results of the proposed scheme are depicted in Table 6 using watermark (Peugeot logo) and host image (Lena and Plane), and in Table 7 using watermark (8Color image) and host image (House and Pepper).

14

S. Sharma, H. Sharma and J.B. Sharma / Applied Soft Computing Journal 84 (2019) 105696

Table 4 The NC of the extracted watermark for a proposed scheme using the Peugeot logo and RTU logo as watermark under various attacks. Watermark image

Peugeot logo

RTU logo

Attack

Parameter

Lena

Plane

House

Lena

Plane

House

Salt & pepper noise

(density=0.5) (density=0.2) (density=0.01) (M=0.1, var=0.5) (M=0.1, var=0.2) (M=0.1, var=0.01) (var=0.5) (var=0.2) (var=0.01) (2, 2) (3, 3) (5, 5) (2, 2) (3, 3) (5, 5) (2, 2) (3, 3) (5, 5) (2, 2) (3, 3) (5, 5) (thresh=50,order=1) (thresh100,order=1) (Q=10) (Q=20) (Q=30) (Q=40) (Q=50) (Q=60) (Q=70) (Q=80) (Q=90) (Q=100) (5:1) (10:1) (20:1) (unsharp)

0.9554 0.9848 0.9966 0.9504 0.9677 0.9965 0.9650 0.9848 0.9964 0.9957 0.9959 0.9958 0.9959 0.9955 0.9945 0.9957 0.9948 0.9926 0.9960 0.9955 0.9946 0.9778 0.9897 0.9954 0.9958 0.9958 0.9960 0.9960 0.9961 0.9962 0.9963 0.9964 0.9964 0.9964 0.9963 0.9961 0.9914 0.9725 0.9462 0.9025 0.9914 0.9912 0.9962 0.9955 0.8922 0.9648 0.9893 0.9778 0.9700 0.8360 0.9918 0.9884

0.9213 0.9722 0.9978 0.9341 0.9679 0.9903 0.9257 0.9691 0.9979 0.9975 0.9965 0.9955 0.9977 0.9974 0.9966 0.9975 0.9965 0.9938 0.9976 0.9972 0.9962 0.9715 0.9894 0.9975 0.9976 0.9977 0.9977 0.9977 0.9977 0.9978 0.9978 0.9979 0.9980 0.9979 0.9979 0.9977 0.9906 0.9083 0.9598 0.8893 0.9827 0.9853 0.9978 0.9979 0.8534 0.9559 0.9797 0.9697 0.9445 0.7870 0.9800 0.9624

0.9371 0.9770 0.9972 0.9323 0.9559 0.9891 0.9488 0.9778 0.9972 0.9734 0.9724 0.9968 0.9969 0.9967 0.9961 0.9968 0.9964 0.9952 0.9968 0.9965 0.9960 0.9828 0.9930 0.9964 0.9966 0.9968 0.9968 0.9968 0.9969 0.9969 0.9969 0.9970 0.9970 0.9970 0.9969 0.9968 0.9960 0.9730 0.9309 0.9065 0.9901 0.9897 0.9970 0.9966 0.9104 0.9690 0.9839 0.9763 0.9626 0.8495 0.9868 0.9734

0.8752 0.9536 0.9961 0.8659 0.9042 0.9945 0.9072 0.9657 0.9960 0.9955 0.9950 0.9955 0.9956 0.9951 0.9937 0.9953 0.9940 0.9908 0.9956 0.9951 0.9940 0.9651 0.9857 0.9953 0.9955 0.9956 0.9957 0.9958 0.9958 0.9959 0.9959 0.9960 0.9961 0.9961 0.9960 0.9957 0.9817 0.9204 0.9411 0.8481 0.9818 0.9845 0.9959 0.9920 0.8134 0.9669 0.9801 0.9719 0.9363 0.8112 0.9872 0.9744

0.8195 0.9249 0.9969 0.8171 0.8722 0.9941 0.8263 0.9169 0.9961 0.9958 0.9920 0.9925 0.9971 0.9968 0.9955 0.9968 0.9951 0.9902 0.9970 0.9964 0.9948 0.9502 0.9817 0.9970 0.9971 0.9971 0.9972 0.9971 0.9971 0.9972 0.9972 0.9972 0.9973 0.9973 0.9972 0.9971 0.9735 0.8147 0.8569 0.8352 0.9580 0.9626 0.9972 0.9896 0.8492 0.9540 0.9584 0.9517 0.9036 0.7812 0.9502 0.9208

0.8240 0.9240 0.9962 0.8493 0.8097 0.9919 0.8481 0.9300 0.9961 0.9961 0.9960 0.9960 0.9962 0.9960 0.9953 0.9961 0.9955 0.9948 0.9961 0.9958 0.9951 0.9708 0.9897 0.9956 0.9960 0.9960 0.9960 0.9961 0.9961 0.9962 0.9962 0.9962 0.9963 0.9963 0.9962 0.9961 0.9905 0.8770 0.8634 0.8413 0.9548 0.9731 0.9962 0.9941 0.8721 0.9488 0.9687 0.9545 0.9146 0.7563 0.9616 0.9387

Gaussian noise

Speckle noise

Gaussian filter

Median filter

Average filter

Weiner filter

Butterworth lowpass filter JPEG compression

JPEG2000 compression

Sharpening Histogram Equalization Gamma correction Rotation Scaling Cropping Cut Shear Translation

(gamma=0.3) (gamma=0.25) ◦ (clockwise 5 ) ◦ (anticlockwise 5 ) (upscale 4 times) (downscale 2 times) (50%) (25%) (Columns=10) (Columns=20) (x=0.1,y=0.1) (x=1,y=0.2) (10, 10) (20, 20)

5.3.3. Comparison with optimized color watermarking schemes Gupta.et al. in [20] proposed a color watermark insertion into a color host image using ABC optimized scaling parameter. DWT and SVD transforms are used for embedding and extraction purpose. But in [20] there is no discussion about the security threat of false positive error, i.e. the false positive error has not been analyzed by this watermarking scheme. In [47] a 64 x 64 binary logo is used to embed in the 512 x 512 color host image using DWT and genetic algorithm. The proposed watermarking scheme is also compared with these related schemes and the results of NC value under different attack are shown in Table 7. The proposed approach clearly outperforms the robustness results as compared with other optimization based watermarking schemes, color watermarking schemes without optimization and color watermarking scheme with optimization as shown in Tables 5–8.

5.4. Multiple attacks Combinations of one or more attack at the same time on the watermarked image are known as multiple attacks. The unauthorized person always tries to apply multiple attacks to extract the embedded watermark, which makes it important to experiment with multiple attacks on the proposed scheme. However, none of the scheme other than [17,42] has discussed the effect of multiple attacks on the watermarked images. The proposed scheme is also tested by applying multiple attacks. The visual results after applying multiple attacks on the watermarked image taking the Peugeot logo and RTU logo as a watermark on the host Lena image are shown in Figs. 21 and 22 respectively. The NC values taking color host images (Lena, Plane, and House) by embedding watermark images (Peugeot logo and RTU logo) under multiple attacks are depicted in Table 9, which shows that the proposed scheme is robust against the multiple attacks. Table 9 inferred that NC values for all the multiple attacks are

S. Sharma, H. Sharma and J.B. Sharma / Applied Soft Computing Journal 84 (2019) 105696

15

Table 5 The comparative analysis of NC values for robustness of the proposed scheme with other related watermarking schemes using Lena as host and Peugeot logo as watermark image. Attack

Proposed scheme

Salt & pepper noise(δ = 0.001) Salt & pepper noise(δ = 0.01) Salt & pepper noise(δ = 0.1) Gaussian noise(M=0, var=0.001) Gaussian noise(M=0, var=0.01) Gaussian noise(M=0, var=0.1) Speckle noise(var=0.001) Speckle noise(var=0.01) Speckle noise(var=0.1) Gaussian filter(3, 3) Gaussian filter(5, 5) Median filter(3, 3) Median filter(5, 5) Average filter (3, 3) Weiner filter(2, 2) JPEG compression (Q=10) JPEG compression (Q=20) JPEG compression (Q=50) Sharpening Histogram Equalization Gamma correction(0.8) ◦ Rotation (angle=2 ) ◦ Rotation (angle=5 ) Scaling (zoomout=0.5, zoomin=2) Scaling (zoomout=0.25, zoomin=4) Translation (10,20) Translation (20,35) Translation (50,50)

0.9965 0.9916 0.9832 0.9965 0.9914 0.9812 0.9964 0.9899 0.9813 0.9959 0.9958 0.9955 0.9945 0.9948 0.9960 0.9954 0.9958 0.9960 0.9914 0.9725 0.9890 0.9947 0.9914 0.9948 0.9903 0.9894 0.9853 0.9728

Ramanjaneyulu

Ansari et al. [2]

Vali et al. [21]

Abdelhakim et al. [22]

Ali et al. [40]

Ansari et al. [23]

0.9970 – – – 0.9466 – – – – 0.9918 – – – 0.9746 0.9967 – – 0.9994 0.9479 0.9878 0.9960 – – 0.9871 – – – –

0.9962 0.9688 0.8924 0.9838 0.9304 0.9179 0.9953 0.9666 0.9210 0.9832 0.9899 0.9716 0.9603 0.9496 0.9940 0.9733 0.9815 0.9938 0.9767 0.9721 0.9973 0.9921 – 0.9470 0.8289 0.9938 0.9917 0.9880

– 0.802 0.544 0.691 0.697 – – – – 0.966 0.965 0.859 – 0.881 – – 0.999 0.978 0.899 0.802 – – – 0.904 – – – –

– 0.8904 – 0.9830 – – – – – 0.9864 – 0.9076 – – – – – 0.9574 0.9972 0.9982 0.9663 – – 0.9134 – – – –

0.9989 – – – 0.9446 – – – – 0.9921 – 0.9896 – 0.9751 0.9955 – – 0.9996 0.9481 0.9878 0.9949 0.9782 0.9843 0.9889 – – – –

et al. [4] 0.9263 – – 0.3992 – – – – – 0.9069 – 0.8130 – 0.6884 0.8447 – – 0.8299 0.7287 0.8880 0.9893 – 0.8021 – – – – –

Table 6 The comparative analysis of NC values for robustness with the proposed scheme by other schemes applying various attacks taking the Peugeot logo as a watermark image. Attack

Host image

Su et al. [45]

Roy et al. [17]

Golea et al. [34]

Jia et al. [26]

Su et al. [35]

Salt & Pepper noise (density=0.02)

Lena Plane

0.9966 0.9975

Proposed scheme

0.9541 0.9538

0.9888 –

0.5698 0.5279

0.8093 0.8089

0.9873 0.9941

Gaussian noise (M=0.1,var=0.001)

Lena Plane

0.9882 0.9902

0.9660 0.9728

0.9270 –

0.8600 0.8188

0.7084 0.7089

0.8931 0.9372

Median filter (5, 1)

Lena Plane

0.9958 0.9974

0.9899 0.9895

0.9332 –

0.5019 0.5168

0.8578 0.9014

0.9187 0.9266

Butterworth lowpass filter (100, 1)

Lena Plane

0.9897 0.9902

0.9715 0.9228

– –

0.5477 0.5855

0.8901 0.8622

0.9577 0.9442

JPEG compression (Q=30)

Lena Plane

0.9958 0.9977

0.8213 0.8410

0.9650 –

0.6531 0.8204

0.8085 0.8186

0.9591 0.9355

JPEG 2000 compression (5:1)

Lena Plane

0.9964 0.9979

0.9910 0.9953

– –

0.9390 0.9375

0.9949 0.9959

0.9988 0.9991

Scaling (upscale 4)

Lena Plane

0.9962 0.9978

0.9917 0.9668

0.9301 –

0.8385 0.8689

0.9962 0.9959

0.9949 0.9927

Sharpening (1.0)

Lena Plane

0.9931 0.9916

0.8071 0.9822

0.9349 –

0.7808 0.6256

0.8735 0.8648

0.9998 0.9945

Bluring (0.2)

Lena Plane

0.9965 0.9979

1.0000 0.9889

– –

1.0000 0.5719

0.9912 0.9958

1.0000 0.9986

Cropping (50%)

Lena Plane

0.8922 0.8561

0.6467 0.5632

0.7645 –

0.5331 0.5311

0.5047 0.5024

0.8488 0.8488

higher than 0.9 except for JPEG compression (Q=50) and shear (x=1, y=0.2) multiple attack. 5.5. False positive error False positive error is the extraction of a wrong watermark from the watermarked image, which is never being inserted into the host image [25]. This error occurs when an unauthorized user wants to prove his/her copyright over the host image, by somehow knowing the embedding and extraction algorithm of the watermarking scheme. To avoid false positive error in the proposed algorithm, both the host and watermark images are

first scrambled by Arnold chaotic map, thereafter the embedding strength factor α is generated by considering both the host and watermark image in perceptual quality metric parameter PSNR. The value of the quality metric PSNR is optimized by the ABC algorithm after applying different attacks. In the proposed scheme the watermark is not inserted into singular values of the encrypted host image, but it is inserted into the principal components which completely ensure to overcome the false positive error. The experiment for false positive error is conducted by taking Lake image [46] as a false watermark, which is tried to be extracted from a watermarked image in which the Peugeot logo has been embedded. False watermark extraction is performed by

16

S. Sharma, H. Sharma and J.B. Sharma / Applied Soft Computing Journal 84 (2019) 105696

Table 7 The comparative analysis of NC values for robustness with the proposed scheme by other schemes applying various attacks taking the 8Color image as watermark. Attack

Host image

Proposed scheme

Su et al. [45]

Roy et al. [17]

Golea et al. [34]

Jia et al. [26]

Su et al. [35]

Salt & Pepper noise (density=0.02)

House Pepper

0.9933 0.9952

0.9311 0.9287

– 0.9122

0.6842 0.6690

0.7483 0.7544

0.9928 0.9902

Gaussian noise (M=0.1,var=0.001)

House Pepper

0.9738 0.9706

0.9389 0.9256

– 0.9387

0.8188 0.7946

0.8889 0.6914

0.8515 0.9131

Median filter (5, 1)

House Pepper

0.9977 0.9968

0.5584 0.5743

– 0.9356

0.6553 0.5722

0.8545 0.7422

0.9416 0.8814

Butterworth Lowpass filter (100,1)

House Pepper

0.9828 0.9702

0.8917 0.9409

– –

0.6171 0.5298

0.9601 0.9078

0.9788 0.9501

JPEG compression (Q=30)

House Pepper

0.9978 0.9968

0.7571 0.8594

– 0.9789

0.7483 0.7038

0.8203 0.7965

0.8663 0.8469

JPEG 2000 compression (5:1)

House Pepper

0.9984 0.9973

0.9937 0.9840

– –

0.8917 0.9419

0.9945 0.9999

0.9998 0.9974

Scaling (upscale 4)

House Pepper

0.9983 0.9971

0.9794 0.9751

– 0.9321

0.7191 0.8401

0.9309 0.9962

0.9978 0.9867

Sharpening (1.0)

House Pepper

0.9789 0.9638

0.9984 0.9877

– 0.9366

0.7852 0.7916

0.9692 0.8908

0.9971 0.9999

Blurring (0.2)

House Pepper

0.9984 0.9975

0.9983 0.9893

– –

0.9191 0.9796

0.9683 0.9922

1.0000 1.0000

Cropping (50%)

House Pepper

0.8220 0.6747

0.9200 0.9200

– 0.9447

0.5213 0.5253

0.5024 0.5001

0.9604 0.9604

Table 8 The comparative analysis of NC values for robustness with the proposed scheme by other schemes applying various attacks taking the RTU logo as a watermark. Attack

Host image

Proposed scheme

Gupta et al. [20]

Vahedi et al. [47]

Salt & Pepper noise (density=0.02)

Lena Pepper

0.9816 0.9815

– –

0.9149 0.9010

Gaussian noise (M=0.1,var=0.001)

Lena Pepper

0.9772 0.9815

0.84 0.80

0.9204 0.9196

Median filter (5, 5)

Lena Pepper

0.9816 0.9778

0.64 0.63

0.9029 0.9053

Butterworth lowpass filter (100, 1)

Lena Pepper

0.9848 0.9865

– –

– –

JPEG compression (Q=30)

Lena Pepper

0.9958 0.9878

0.85 0.84

– –

JPEG 2000 compression (5:1)

Lena Pepper

0.9944 0.9919

– –

– –

Scaling (upscale 1.2)

Lena Pepper

0.9953 0.9918

0.99 0.99

0.9388 0.9633

Sharpening (1.0)

Lena Pepper

0.8516 0.8981

– –

– –

Blurring (0.2)

Lena Pepper

0.9961 0.9952

– –

– –

Cropping (50%)

Lena Pepper

0.8134 0.8476

0.61 0.60

0.7803 0.7654

Table 9 NC value after applying multiple attacks. Watermark image

Peugeot logo

Multiple attacks

Lena

Plane

House

RTU logo Lena

Plane

House

Salt & pepper noise (density=0.2) + JPEG compression (Q=30) ◦ Histogram Equalization + Rotation (clockwise 5 ) Gaussian noise (M=0.1,var=0.2) + Cropping (25%) Sharpening + Average filter (5, 5) Speckle noise (var=0.2) + Shear (x=0.1, y=0.1) Wiener filter (5, 5) + Translation (10, 10) Gaussian filter (5, 5) + Cut (10 columns) Gamma correction (gamma=0.25) + JPEG compression (Q=50) ◦ Average filter (5, 5) + Rotation (anticlockwise 5 ) JPEG compression (Q=50) + Shear (x=1, y=0.2) Salt & pepper noise (density=0.2) + Gaussian noise (M=0.1,var=0.2) Histogram equalization + Gamma correction (gamma=0.25)

0.9847 0.9692 0.9645 0.9944 0.9702 0.9880 0.9887 0.9014 0.9898 0.8569 0.9628 0.9713

0.9520 0.9077 0.9487 0.9964 0.9441 0.9643 0.9801 0.8967 0.9873 0.7908 0.9509 0.9903

0.9693 0.9603 0.9503 0.9963 0.9626 0.9733 0.9838 0.9355 0.9901 0.8798 0.9488 0.9797

0.9888 0.9559 0.9580 0.9936 0.9650 0.9918 0.9877 0.9012 0.9902 0.8227 0.9728 0.9387

0.9772 0.8979 0.9406 0.9957 0.9497 0.9844 0.9830 0.9148 0.9884 0.7975 0.9469 0.9892

0.9848 0.9513 0.9392 0.9951 0.9511 0.9864 0.9826 0.9101 0.9897 0.7677 0.9579 0.9586

S. Sharma, H. Sharma and J.B. Sharma / Applied Soft Computing Journal 84 (2019) 105696

Fig. 23. The result of a false positive error experiment (a) Watermarked image (using Peugeot logo as a watermark) (b) Lake (false watermark) (c) False extracted image.

considering the false values of Arnold chaotic map key and secret embedding strength factor α , the results of the visual perception are shown in Fig. 23. 6. Conclusions An adaptive and robust color image watermarking scheme using ABC optimized quality metric is proposed in this paper. The host and watermark images taken are color images of the same dimension, which increases the watermark embedding capacity into the host image. In the proposed scheme extra security is provided by scrambling both host and watermark images using Arnold chaotic map. The adaptive embedding strength factor is generated by considering both the host and watermark image in perceptual quality metric parameter PSNR, which is further optimized by the ABC algorithm. In order to correctly extract the embedded watermark, some information obtained during the embedding phase is also required, it shows the semi-blind nature of the proposed scheme. Due to image adaptive embedding strength factor, the imperceptibility results are better than other state-of-the-art watermarking schemes. The robustness of the proposed scheme is evaluated by applying different image enhancement technique based manipulations such as noise addition, filtering, sharpening, etc., and geometrical attacks such as JPEG compression, rotation, scaling, etc. Experimental results prove that the proposed scheme is able to bear all these attacks, and correctly extracts the watermark. The comparative analysis shows that the proposed scheme outperforms the other related watermarking schemes in imperceptibility and robustness. Also, the proposed scheme is free from the major problem of false positive error, that occurs in most of the SVD based watermarking schemes. Declaration of competing interest No author associated with this paper has disclosed any potential or pertinent conflicts which may be perceived to have impending conflict with this work. For full disclosure statements refer to https://doi.org/10.1016/j.asoc.2019.105696. CRediT authorship contribution statement Sourabh Sharma: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - original draft, Writing - review & editing. References [1] A.M. Abdelhakim, H.I. Saleh, A.M. Nassar, A quality guaranteed robust image watermarking optimization with artificial bee colony, Expert Syst. Appl. 72 (2017) 317–326. [2] I.A. Ansari, M. Pant, C.W. Ahn, ABC optimized secured image watermarking scheme to find out the rightful ownership, Optik 127 (2016) 5711–5721.

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