A hybridized methodology of different wavelet transformations targeting medical images in IoT infrastructure

Measurement 148 (2019) 106813

Contents lists available at ScienceDirect

Measurement journal homepage: www.elsevier.com/locate/measurement

A hybridized methodology of different wavelet transformations targeting medical images in IoT infrastructure Tamara K. Al-Shayea a, Constandinos X. Mavromoustakis a,⇑, Jordi Mongay Batalla b, George Mastorakis c a

Department of Computer Science, Mobile Systems Laboratory (MoSysLab), University of Nicosia and University of Nicosia Research Foundation (UNRF), Cyprus, Nicosia National Institute of Telecommunications and Warsaw University of Technology, Szachowa Str. 1 and Nowowiejska Str. 15/19, Warsaw, Poland c Department of Management Science and Technology, Hellenic Mediterranean University, Agios Nikolaos, Crete, Greece b

a r t i c l e

i n f o

Article history: Received 8 March 2019 Received in revised form 28 May 2019 Accepted 9 July 2019 Available online 12 July 2019 Keywords: Medical image watermarking Biorthogonal wavelet Reverse biorthogonal wavelet Discrete meyer wavelet Symlets wavelet Coiflets wavelet

a b s t r a c t The Internet of Things (IoT) paradigm has become a vital part of all significant scientific sectors, including the healthcare domain. Medical images in the healthcare sector are indispensable items that are usually susceptible to distortion once they are shared and transferred via the Internet. The sector faces the distinct and constant challenge of preserving medical data, which can be manipulated by various malicious attacks, in turn potentially compromising the patients’ diagnostic data. In this situation, such medical data ought to be private, with access only granted to patients and physicians. This paper elaborates on a hybrid measurement technique for digital image watermarking that utilizes medical images (X-ray, MRA, and CT), which are an extremely robust method for protecting clinical information. The authors explore various different wavelet families, in addition to hybridization between these wavelets. These are carried out on three levels decomposition of Discrete wavelet transformation (biorthogonal 6.8 wavelets, biorthogonal 3.5 wavelets, biorthogonal 5.5 wavelets, reverse biorthogonal 6.8, reverse biorthogonal 3.5, reverse biorthogonal 5.5, discrete meyer, symlets 5, symlets 8 coiflets 4 wavelet, and coiflets 5 wavelet transform). Each level uses various types of wavelet transformation to present the watermarked image, and then extracts the medical watermark from the original watermarked image. The results of diverse types of attack have been compared, while the proposed measurement technique’s performance is evaluated using statistical parameters (MSE, PSNR, SSIM, and NC). This in turn measures the quality of the image, which so far shows promising results. Ó 2019 Elsevier Ltd. All rights reserved.

1. Introduction The security and integrity of medical data has become a big challenge for healthcare service applications, due to the significant advancement of the IoT in the healthcare sector [1]. The core technology that enables the design and process of smart healthcare, relies on the framework provided by the IoT, for the realization of the vision of smart healthcare as part of a smart city, whereby the smart technology, communication, energy grids, premises, information technology, and transportation work simultaneously. There are certain challenges faced in the IoT infrastructure, particularly user authentication in the context of automated analysis of biomedical images, secure communication, communication of the analysis results and related metadata in a smart healthcare framework [2]. Due to its affordability and potential to develop compact systems IoT has a wide scope in the healthcare system [3]. Information can be extracted and processed in a safe manner by water⇑ Corresponding author. https://doi.org/10.1016/j.measurement.2019.07.041 0263-2241/Ó 2019 Elsevier Ltd. All rights reserved.

marking, which means hiding the data known as a watermark in digital media. Methods and technologies that hide information in digital media are describable by digital watermarking [4]. In the process of protecting or resolving the copyright integrity of video, audio, and multimedia data files, a watermarking is considered a necessary field [5]. While digital watermarking can be applied to image [6–8], audio [9,10] and video [11–13], our work focuses on medical image watermarking. A comparative study of embedded zero tree wavelet, spatial orientation tree wavelet, set partition in hierarchical tree, set partition in hierarchical tree-3D, wavelet level Metropolis Monte Carlo, global thresholding of coefficients and Huffman encoding, global thresholding of coefficients and fixed encoding algorithms, is done by means of studying the different wavelets namely, haar, daubechies, symlets, coiflets, biorthogonal, reverse biorthogonal and discrete meyer for each of the algorithms mentioned above. It is also uses specific image quality measurement techniques, such as, PSNR and MSE [14]. Singh and Dutta [15] submitted a Wavelet transform–Singular Value Decomposition, based robust

2

T.K. Al-Shayea et al. / Measurement 148 (2019) 106813

zerowatermarking technique to address the privacy and security issues of medical images. Al-Haj and Abdel-Nabi [16] proposed that a crypto-watermarking algorithm has achieved high embedding capacity in medical images, and that this occurs via joining the watermarked image and its encrypted version in the incorporated image after embedding two different watermarks. Shehab et al. [17] presented a SVD based fragile watermarking scheme, which offers more security and provides a supplementary way to locate the attacked areas inside different medical images, utilizing the grouped block method. A watermarking algorithm has been suggested by Liu and Tan [18] constructed on the singular value decomposition of an image and indicating its high robustness against image distortion. Rykaczewski [19] and Zhang and Li [20] presented by Liu and Tan [18] have a very high possibility of false positive answers. Dong et al. [21] proposed a frequency domain digital watermark algorithm implemented in a spatial domain, which is based on correlation coefficient and quadratic DCT transform. A watermarking scheme based on the singular value decomposition and human visual system (HVS) has been proposed by Makbol et al. [22]. Tyagi and Singh [23] used the genetic algorithm concept in the discrete cosine transform domain. Ramanjaneyulu and Rajarajeswari [24] submitted an image watermarking algorithm based on DWT. A digital watermarking algorithm has been developed by Shuai Liu et al. [25] which is based on a fractal encoding method and the DCT. Veni and Meyyappan [26] proposed a blind watermarking algorithm that combines using DWT and DCT. There are also different watermarking techniques, such as the Discrete Cosine Transform (DCT), Singular Value Decomposition (SVD) and Bacterial Foraging Optimization Algorithm (BFOA). These techniques are performed to the fused biometric feature image by Anu et al. [27]. Bhuyan et al. [28] and Verma [29] used a hybridization of DWT and SVD in watermarking. From this perspective, this paper proposes an algorithm for hiding a healthcare sign into a medical digital image without observing any changes in appearance or evident distortion in the host image. The proposed algorithm uses bior6.8, bior3.5, bior5.5, rbio6.8, rbio3.5, rbio5.5, dmey, sym5, sym8 coiflets4, and coiflets5 wavelet transform separately. There is also a hybridization between these wavelet families (dmey, rbio6.8 and Coiflets5), (bior3.5, bior5.5 and rbio6.8), and (bior3.5, bior5.5and Symlets5). Afterwards, the medical watermark image is extracted from the watermarked image without loss or distortion of any of its contents. Finally, several attack methods are conducted on the host image as experimentally. Four statistical parameters are used to measure the results of these experiments. The proposed algorithm has higher performance characteristics, such as robustness and peak signal to noise ratio, than traditional methods which are shown by using statistical parameter results. The rest of this paper is organized as follows: Section 2 gives the related work while Section 3 gives the methodology and presents the digital image watermarking techniques. Section 4 presents the proposed image watermarking technique. Section 5 provides the results and the performance evaluation of the proposed technique. Finally, section 6 concludes this study.

2. Related work Some researchers have proposed medical image watermarking algorithms in an IoT environment. Usmonov et al. [30] addressed the issue of cybersecurity when using IoT. They presented an algorithm to protect data transmitted between the logical, virtual, and physical components of infrastructural IoT. Sarwar et al. [31] proposed a fragile zero watermarking approach, which can cope with the limitations on Elliptic Curve Cryptography (ECC)-based schemes. Ferdowsi and Saad [32] presented a dynamic watermark-

ing algorithm using deep learning schemes over the IoT, in order to prevent cyber attacks. They also stated that the simulation results have shown that the proposed LSTM watermarking method can also detect the existence of complicated attacks. Singh [33] presented a review of secure medical image watermarking, focusing on the problem of unauthorized access to medical content which is to be transmitted and received via the internet. Ko et al. [34] submitted a medical image watermarking algorithm, using fractional discrete cosine transform (FDCT). They proved that FDCT- based watermarking is preferable to Quantization Index Modulation (QIM) based watermarking for medical images. Mehta et al. [35] studied the performance of DWT, SVD, and DWT-SVDbased watermarking schemes. They found that the DWT-based algorithm is more suitable for medical images than the SVD. Aparna and Kishore [36] have produced a medical image watermarking algorithm using a region that grows and utilizes the fingerprint biometric. Experimental results show that a watermarked image quality has an average PSNR value of 44 dB. A zero-watermarking algorithm is proposed by Ali et al. [37] in order to embed the identity of a person, without making any distortion in medical speech signals. Identity is inserted in a secret key instead of a signal by using a 1-D local binary operator. Singh and Patel [38] used discrete wavelet transform and discrete cosine transform on medical images watermarking algorithm. Experimental results submitted a PSNR value for the watermark and extracted a watermark image in the range of 33.45 dB to 40.15 dB. Hazzaa and Ahmed [39] used several medical images to embed the watermarked image as a solution to security and privacy issues in telemedicine. Discrete wavelet transform is implemented on the proposed watermarking scheme, but no acceptable result has been shown. Al-Haj et al. [40] submitted an algorithm which combines encryption and digital watermarking techniques that can provide secure transmission of medical images over the vulnerable internet. 3. Methodology The aim of digital watermarking is to embed secret data into the image without significantly impacting the visual quality. Digital watermarking has been suggested as a way that can achieve digital protection [22]. There are a number of properties typically associated with an image watermark process [41]: 3.1. Nonblindness A watermarking method is said to be nonblind if it requires access to the host-unwatermarked image to extract the watermark. Conversely, a watermarking method is said to be blind if the host image is not needed for recovering the watermark. 3.2. Imperceptibility An imperceptible watermark is embedded into a host image by complicated algorithms and is imperceptible to the naked eye. It is possible, however, that this could be extracted by a computer. A watermarking is said to be perceptible if the embedded watermark is intended to be visible. 3.3. Robustness Watermark robustness enables the hidden watermark to survive legal daily usage or image processing operations, such as intentional or unintentional attacks. Robust, semifragile, and fragile are the three types of watermarks according to embedding purposes.

T.K. Al-Shayea et al. / Measurement 148 (2019) 106813

where f ðx; yÞ denotes an image in the spatial domain and F ðu; v Þ denotes an image in frequency domain.

3.4. Security If authorized users can detect the watermark, then it is said to be private. In contrast, watermarking methods that allow anyone to detect the watermark are called public. As a default, private watermarking methods are more robust than public ones, in which any attack can easily destroy or remove the data embedded. Least-significant-bit (LSB) insertion is the simplest method in the spatial domain, while discrete wavelet transform (DWT), discrete cosine transform (DCT) and discrete Fourier transform (DFT) are often used in image watermarking, for embedding watermarks into a host image. Many researchers presented works in spatial and frequency domain. However, the frequency domain is more robust than the spatial domain. The discrete wavelet transform (DWT) has also become a powerful method in watermarking [42]. Wavelet fundament is a doubly indexed family of L2(R) functions. The mother wavelet function is:

1 t  k2j wj;k ðt Þ ¼ pffiffiffiffi w 2j 2j

!

ð1Þ

where j is the scale parameter and k is the shift parameter both, of these are integers [43]. A sufficient condition for the reconstruction of any signal x of finite energy by the formula:

xð t Þ ¼

XX ðx; wj;k Þ:wj;k ðtÞ j2z

ð2Þ

k2z

This is the function {wj;k :j; k 2 z} which forms an orthonormal basis of L2(R). Wavelet has several families that have proven to be useful namely Haar, Daubechies, Biorthogonal, Coiflets, Meyer, Morlet, Reverse biorthogonal, Mexican hat and Symlets. The significant properties of wavelet families differ in several ways that include [44]: A. The wavelet in time and frequency and rate of decay are supported. B. Symmetry or antisymmetry of the wavelet. The accompanying perfect reconstruction filters which have linear phase. C. The number of vanishing moments. Wavelets with increased numbers of vanishing moments result in sparse representations for a big class of signals and images. D. The regularity of the wavelet, since smoother wavelets supply sharper frequency decision. Furthermore, repeated algorithms for wavelet construction converge more rapidly. E. Presence of a scaling function, u. There is a signal which transforms from the spatial domain to the frequency domain. This happens due to the Discrete Cosine Transform (DCT). Of the watermarking schemes, the DCT based watermarking is considered more robust compared to the spatial domain technique. The general form for a two-dimension discrete cosine transform is defined in an equation, and the corresponding inverse transformation is obviously defined in equation as follows [41]:

F ðu; v Þ ¼ C ðuÞC ðv Þ

3

N 1 X N 1 X x¼0 y¼0

f ðx; yÞcos

    ð2x þ 1Þup ð2y þ 1Þv p cos 2N 2N

4. The proposed scheme The proposed medical image watermarking algorithm utilizes the family of the wavelets which include biorthogonal 6.8 wavelets, biorthogonal 3.5 wavelets, biorthogonal 5.5 wavelets, reverse biorthogonal 6.8, reverse biorthogonal 3.5, reverse biorthogonal 5.5, discrete meyer, symlets 5, symlets 8 coiflets 4 wavelet, and coiflets 5 wavelet transform. Hybridization between these wavelet families is proposed in this work. The first hybridization is completed using discrete meyer in the first level, reverse biorthogonal 6.8 in the second level, and coiflets 5 in the third level for both the host and the watermark image. The second hybridization is done by using biorthogonal 3.5 in the first level, biorthogonal 5.5 in the second level, and reverse biorthogonal 6.8 in the third level for both host and watermark image. The third hybridization is completed using biorthogonal 3.5 in the first level, biorthogonal 5.5 in the second level, and symlets 5 in the third level for both host and watermark image. A biorthogonal wavelet is known as the wavelet where the connected wavelet transform is invertible, but not necessarily orthogonal. The orthogonal wavelets are allowed fewer degrees of freedom than the design of the biorthogonal wavelets allows. One additional degree of freedom that has been supplied by the designed biorthogonal wavelets, is the potential to establish symmetrical wavelet functions. While the characteristics of the two wavelet families are similar, the symlets are nearly symmetrical wavelets that have been presented by Ingrid Daubechies as an adjustment to the db family [45]. In accordance with high and low frequency, Fig. 1 shows that in DWT, the image is disintegrated into four equal frequency subbands (LL1, LH1, HL1, and HH1) [46]. Fig. 2 shows wavelet and scaling function for biorthogonal 6.8 (bior6.8) wavelet transform, while Fig. 3 shows wavelet, and scaling function for biorthogonal 3.5 (bior3.5) wavelet transform. Fig. 4 shows wavelet, and scaling function for biorthogonal 5.5 (bior5.5) wavelet transform. Fig. 5 shows wavelet, and scaling function for reverse biorthogonal 6.8 (rbio6.8) wavelet transform. Fig. 6 shows wavelet and scaling function for reverse biorthogonal 3.5 (rbio3.5) wavelet transform. Fig. 7 shows wavelet, and scaling function for reverse biorthogonal 5.5 rbio5.5) wavelet transform. Fig. 8 shows wavelet and scaling function for discrete meyer (dmey) wavelet transform. Fig. 9 shows wavelet and scaling function for symlets5 (sym5) wavelet transform. Fig. 10 shows wavelet, and scaling function for symlets8 (sym8) wavelet transform. Fig. 11 shows wavelet, and scaling function for coiflets4 (coif4) wavelet transform, and finally, Fig. 12 shows wavelet, and scaling function for coiflets5 (coif5) wavelet transform [47] (see Fig. 13). In Fig. 14 the Block diagram of the proposed watermark hybridizing for extracting techniques is shown with the respective steps. Algorithm 1 illustrates the pseudocode for the proposed watermark hybridizing for embedding while algorithm 2 illustrates the

ð3Þ

qffiffiffi where if u = v = 0, C(u) = C(v)= N1 ; otherwise, u = v = 0, C(u) = C(v) qffiffiffi = N2 .     N 1 X N 1 X ð2x þ 1Þup ð2y þ 1Þv p f ðx; yÞ ¼ C ðuÞC ðv Þf ðu; v Þcos cos 2N 2N u¼0 v ¼0

ð4Þ

Fig. 1. Three levels discrete wavelet decomposition.

4

T.K. Al-Shayea et al. / Measurement 148 (2019) 106813

Fig. 2. Bior6.8. A. Scaling function u. B. Wavelet function w.

Fig. 3. Bior3.5. A. Scaling function u. B. Wavelet function w.

Fig. 4. Bior5.5. A. Scaling function u. B. Wavelet function w.

Fig. 5. Rbio6.8. A. Scaling function u. B. Wavelet function w.

Fig. 6. Rbio3.5. A. Scaling function u. B. Wavelet function w.

Fig. 7. Rbio5.5. A. Scaling function u. B. Wavelet function w.

Fig. 8. Dmey. A. Scaling function u. B. Wavelet function w.

Fig. 9. Symlets5. A. Scaling function u. B. Wavelet function w.

Fig. 10. Symlets8. A. Scaling function u. B. Wavelet function w.

Fig. 11. Coifelts4. A. Scaling function u. B. Wavelet function w.

T.K. Al-Shayea et al. / Measurement 148 (2019) 106813

5

pseudocode for the proposed watermark hybridizing for extracting. The pseudocode for attacking the watermarked image is shown in algorithm 3. Algorithm 1 Pseudocode for Watermark Embedding Algorithm 1 2 3 4 5 6

7

8

9 10 11 12 13

14

15

16 17

18 19

20

21

22

Inputs: Host image, watermarks Output: Watermarked image for each host image H do Read the image. Convert the image to double. Apply the first level of DWT as follows: [LL,LH,HL,HH] = dwt2(H, one of the wavelet families (i.e.: bior6.8, bior3.5, bior5.5, rbio6.8, rbio3.5 rbio5.5, dmey, sym5, sym8, coiflets4 and coiflets5)); Apply the second level of DWT as follows: [LL1,LH1,HL1,HH1] = dwt2(LL, one of the wavelet families (i.e.: bior6.8, bior3.5, bior5.5, rbio6.8, rbio3.5 rbio5.5, dmey, sym5, sym8, coiflets4 and coiflets5)); Apply the third level of DWT as follows: [LL2,LH2,HL2,HH2] = dwt2(LL1, one of the wavelet families (i.e.: bior6.8, bior3.5, bior5.5, rbio6.8, rbio3.5 rbio5.5, dmey, sym5, sym8, coiflets4 and coiflets5)); end for for each watermarks image W do Read the image. Convert the image to double. Apply the first level of DWT as follows: [LLw,LHw,HLw,HHw] = dwt2(W, one of the wavelet families (i.e.: bior6.8, bior3.5, bior5.5, rbio6.8, rbio3.5, rbio5.5, dmey, sym5, sym8, coiflets4 and coiflets5)); Apply the second level of DWT as follows: [LLw1,LHw1,HLw1,HHw1] = dwt2(LLw, one of the wavelet families (i.e.: bior6.8, bior3.5, bior5.5, rbio6.8, rbio3.5, rbio5.5, dmey, sym5, sym8, coiflets4 and coiflets5)); Apply the third level of DWT as follows: [LLw2,LHw2,HLw2,HHw2] = dwt2(LLw1, one of the wavelet families (i.e.: bior6.8, bior3.5, bior5.5, rbio6.8, rbio3.5, rbio5.5, dmey, sym5, sym8, coiflets4 and coiflets5)); end for Watermarkedimage ¼ LL2 þ a  LLw2 where a is the parameter for embedding strength coefficient that controls the embedding strength. (a = 0.01). for each watermarked image do Apply the first level of inverse DWT as follows: Watermarkedimage_level1 = idwt2(Watermarkedimage, LH2,HL2,HH2, one of the wavelet families (i.e.: bior6.8, bior3.5, bior5.5, rbio6.8, rbio3.5, rbio5.5, dmey, sym5, sym8, coiflets4 and coiflets5)); Apply the second level of inverse DWT as follows: Watermarkedimage_level2 = idwt2(Watermarkedimage_ level1,LH1,HL1,HH1, one of the wavelet families (i.e.: bior6.8, bior3.5, bior5.5, rbio6.8, rbio3.5, rbio5.5, dmey, sym5, sym8, coiflets4 and coiflets5)); Apply the third level of inverse DWT as follows: Watermarkedimage_final = idwt2(Watermarkedimage_le vel2,LH,HL,HH, one of the wavelet families (i.e.: bior6.8, bior3.5, bior5.5, rbio6.8, rbio3.5, rbio5.5, dmey, sym5, sym8, coiflets4 and coiflets5)); end for

Fig. 12. Coifelts5. A. Scaling function u. B. Wavelet function w.

Fig. 13. Block diagram of the proposed watermark hybridizing for embedding techniques.

6

T.K. Al-Shayea et al. / Measurement 148 (2019) 106813

Algorithm 2 Pseudocode for Watermark Extraction Algorithm 1 2 3 4 5

6

7

8 9

Input: Watermarked image Output: Extracted watermarks for each watermarked image do Read the watermarked image. Apply the first level of DWT as follows: [a b c d] = dwt2(Watermarked image, one of the wavelet families (i.e.: bior6.8, bior3.5, bior5.5, rbio6.8, rbio3.5, rbio5.5, dmey, sym5, sym8, coiflets4 and coiflets5)); Apply the second level of DWT as follows: [aa bb cc dd] = dwt2(a, one of the wavelet families (i.e.: bior6.8, bior3.5, bior5.5, rbio6.8, rbio3.5, rbio5.5, dmey, sym5, sym8, coiflets4 and coiflets5)); Apply the third level of DWT as follows: [aaa bbb ccc ddd] = dwt2(aa, one of the wavelet families (i.e.: bior6.8, bior3.5, bior5.5, rbio6.8, rbio3.5, rbio5.5, dmey, sym5, sym8, coiflets4 and coiflets5)); end for do the following to extract the image: Extracted image = aaa-LL2;

Algorithm 3 Pseudocode for Attacking the Watermarked Image 1 2 3 4 5 6 7 8 9 10

11 12

13

14

Inputs: Host image, Watermarked image Image Attacked = attack (Watermarked image); e = 0; [m n] = size(Host image); for i = 1:m for j = 1:n e = e + double((Host image(i,j) - ImageAttacked(i,j))^2); end for end for measure host image quality using Mean Square Error (MSE): MSE = e / (m*n); mm = maximum (maximum (Host image)); measure host image quality using Peak Signal-to-Noise Ratio (PSNR): PSNR = 10*log((double(mm)^2)/out); measure host image quality using Structural Similarity Index (SSIM): ssim value = SSIM (Host image, Image Attacked); measure host image quality using Normalized CrossCorrelation (NC): NC = 2-D correlation coefficient (Host image, Image Attacked);

5. Experimental results In this section, we have accomplished an evaluation analysis of the proposed watermark scheme. The algorithms have been performed in a MATLAB-based software. The host medical image and the watermark image that has been used is 512  512 grayscale image. Three types of medical image (X-ray image, MRA image, and CT image) are used as a host image. Fig. 15 shows these medical host images while the watermark image is shown in Fig. 16. A hybridization of three wavelet families has been completed, using three types of medical image as a host image. The first wave-

Fig. 14. Block diagram of the proposed watermark hybridizing for extracting techniques.

let transform is Biorthogonal (Bior3.5), the second wavelet transform is Biorthogonal (Bior5.5), and the last wavelet transform is Symlets (Sym5). Fig. 17 shows the watermarked image and Fig. 18 shows the extracted image of an X-ray medical image. Fig. 19 shows the watermarked image and Fig. 20 shows the extracted image of an MRA medical image. Fig. 21 shows the watermarked image and Fig. 22 shows the extracted image of a CT medical image. The difference between the original pixels in a host image and the watermarked pixels in the watermarked image can be shown by the Mean Square Error (MSE) which illustrates the quality of the distortion level of the watermarked image. The MSE is measured in real value.

MSE ¼

N1 XX 1 M1 ðHðx; yÞ  W ðx; yÞÞ2 MXN x¼0 y¼0

ð5Þ

7

T.K. Al-Shayea et al. / Measurement 148 (2019) 106813

Fig. 15. A. X-ray image. B. Brain image. C. Chest image.

Fig. 19. Watermarked image.

Fig. 16. Watermark image.

Fig. 20. Extracted image.

Fig. 17. Watermarked image.

Fig. 21. Watermarked image.

Fig. 18. Extracted image.

where H is an original host image and W is a watermarked image, respectively. M is the size of the host image and row size and N is column size of the host image, respectively. Peak Signal-to-Noise Ratio (PSNR) finds the difference between the host image and the watermarked image, which is utilized to evaluate the performance of the proposed algorithm. The PSNR is measured in dB value.

PSNR ¼ 10Xlog 10

2552 MSE

!

ð6Þ

Fig. 22. Extracted image.

The quantitative similarity between the watermarked image and the host image is measured by utilizing the Normalized Cross-Correlation (NC).NC is defined as:

PP

NC ¼

i

0 j ½W ði; jÞ : W ði; jÞ PP 2 i j ½W ði; jÞ

ð7Þ

8

T.K. Al-Shayea et al. / Measurement 148 (2019) 106813

Table 1 MSE, PSNR, SSIM, and NC with no attacks. Wavelet

Image

MSE

PSNR

SSIM

NC

Bior6.8 Bior3.5 Bior5.5 Rbio6.8 Rbio3.5 Rbio5.5 Dmey Sym5 Sym8 Coiflets4 Coiflets5 Dmey, Rbio6.8 and Coiflets5 Bior3.5, Bior5.5 and Rbio6.8 Bior3.5, Bior5.5 and Sym5 Bior6.8 Bior3.5 Bior5.5 Rbio6.8 Rbio3.5 Rbio5.5 Dmey Sym5 Sym8 Coiflets4 Coiflets5 Dmey, Rbio6.8 and Coiflets5 Bior3.5, Bior5.5 and Rbio6.8 Bior3.5, Bior5.5 and Sym5 Bior6.8 Bior3.5 Bior5.5 Rbio6.8 Rbio3.5 Rbio5.5 Dmey Sym5 Sym8 Coiflets4 Coiflets5 Dmey, Rbio6.8 and Coiflets5 Bior3.5, Bior5.5 and Rbio6.8 Bior3.5, Bior5.5 and Sym5

X-ray

0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005 0.000005

121.4358 121.4961 121.4430 121.4328 121.3672 121.4233 121.4324 121.4390 121.4347 121.4417 121.4359 121.4323 121.4360 121.4562 120.9827 121.0429 120.9899 120.9796 120.9141 120.9701 120.9755 120.9858 120.9815 120.9886 120.9828 120.9790 120.9828 121.0030 121.2337 121.2940 121.2409 121.2306 121.1651 121.2211 121.2283 121.2368 121.2325 121.2396 121.2338 121.2297 121.2339 121.2541

0.9982 0.9983 0.9983 0.9982 0.9982 0.9982 0.9982 0.9982 0.9982 0.9982 0.9982 0.9982 0.9982 0.9982 0.9970 0.9970 0.9970 0.9970 0.9970 0.9970 0.9970 0.9970 0.9970 0.9970 0.9970 0.9970 0.9970 0.9970 0.9860 0.9861 0.9860 0.9860 0.9860 0.9860 0.9860 0.9860 0.9860 0.9860 0.9860 0.9860 0.9860 0.9860

1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

MRA

CT

Table 2 MSE, PSNR, SSIM and NC of different attacks for bior6.8. Type of attack

Image

MSE

PSNR

SSIM

NC

Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack

X-ray

0.0044 0.00009 0.0002 0.0007 0.0003 0.0201 0.0044 0.0002 0.0005 0.0006 0.0002 0.0237 0.0043 0.0002 0.0007 0.0006 0.0004 0.0303

55.1448 94.0532 86.0653 72.3635 79.7705 40.0087 54.6961 84.8374 75.9765 74.0288 82.6472 37.8982 55.3192 85.8315 73.2726 73.4104 78.5761 35.6942

0.7569 0.9892 0.9835 0.5939 0.9568 0.7055 0.7745 0.9835 0.9760 0.4847 0.9899 0.7313 0.7722 0.9678 0.9503 0.5464 0.9697 0.6095

0.9636 0.9992 0.9983 0.9934 0.9993 0.8274 0.9734 0.9987 0.9968 0.9967 0.9995 0.8534 0.9745 0.9988 0.9957 0.9962 0.9989 0.8141

MRA

CT

9

T.K. Al-Shayea et al. / Measurement 148 (2019) 106813 Table 3 MSE, PSNR, SSIM and NC of different attacks for Bior3.5. Type of attack

Image

MSE

PSNR

SSIM

NC

Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack

X-ray

0.0046 0.00009 0.0002 0.0007 0.0003 0.0201 0.0047 0.0002 0.0005 0.0006 0.0002 0.0237 0.0044 0.0002 0.0007 0.0006 0.0004 0.0303

54.6506 94.0575 86.0682 72.3298 79.7719 40.0082 54.0063 84.8376 75.9751 73.9441 82.6512 37.8993 55.0680 85.8337 73.2715 73.3878 78.5787 35.6949

0.7483 0.9892 0.9835 0.5930 0.9568 0.7055 0.7600 0.9835 0.9760 0.4841 0.9899 0.7313 0.7709 0.9679 0.9504 0.5468 0.9695 0.6096

0.9618 0.9992 0.9983 0.9933 0.9993 0.8273 0.9716 0.9987 0.9968 0.9966 0.9995 0.8534 0.9739 0.9988 0.9957 0.9961 0.9989 0.8141

MRA

CT

Table 4 MSE, PSNR, SSIM and NC of different attacks for Bior5.5. Type of attack

Image

MSE

PSNR

SSIM

NC

Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack

X-ray

0.0044 0.00009 0.0002 0.0007 0.0003 0.0201 0.0046 0.0002 0.0005 0.0006 0.0002 0.0237 0.0042 0.0002 0.0007 0.0007 0.0004 0.0303

55.1923 94.0534 86.0652 72.3485 79.7705 40.0089 54.3101 84.8376 75.9766 74.0406 82.6473 37.8981 55.3937 85.8316 73.2727 73.3614 78.5763 35.6942

0.7618 0.9892 0.9835 0.5929 0.9568 0.7055 0.7646 0.9835 0.9760 0.4866 0.9899 0.7313 0.7787 0.9678 0.9504 0.5464 0.9697 0.6095

0.9637 0.9992 0.9983 0.9933 0.9993 0.8274 0.9724 0.9987 0.9968 0.9967 0.9995 0.8534 0.9747 0.9988 0.9957 0.9961 0.9989 0.8141

MRA

CT

Table 5 MSE, PSNR, SSIM and NC of different attacks for rbio6.8. Type of attack

Image

MSE

PSNR

SSIM

NC

Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack

X-ray

0.0046 0.00009 0.0002 0.0007 0.0003 0.0201 0.0046 0.0002 0.0005 0.0006 0.0002 0.0237 0.0042 0.0002 0.0007 0.0007 0.0004 0.0303

54.6505 94.0531 86.0651 72.3295 79.7705 40.0088 54.3101 84.8374 75.9766 74.0405 82.6471 37.8981 55.3396 85.8315 73.2726 73.3238 78.5760 35.6942

0.7483 0.9892 0.9835 0.5930 0.9568 0.7055 0.7646 0.9835 0.9760 0.4866 0.9899 0.7313 0.7764 0.9678 0.9503 0.5457 0.9697 0.6095

0.9618 0.9992 0.9983 0.9933 0.9993 0.8274 0.9724 0.9987 0.9968 0.9967 0.9995 0.8534 0.9745 0.9988 0.9957 0.9961 0.9989 0.8141

MRA

CT

10

T.K. Al-Shayea et al. / Measurement 148 (2019) 106813

Table 6 MSE, PSNR, SSIM and NC of different attacks for rbio3.5. Type of attack

Image

MSE

PSNR

SSIM

NC

Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack

X-ray

0.0044 0.00009 0.0002 0.0007 0.0003 0.0201 0.0044 0.0002 0.0005 0.0006 0.0002 0.0237 0.0041 0.0002 0.0007 0.0006 0.0004 0.0303

55.1447 94.0520 86.0684 72.3637 79.7697 40.0074 54.8525 84.8344 75.9744 74.0243 82.6476 37.8994 55.7886 85.8306 73.2704 73.4186 78.5759 35.6950

0.7569 0.9892 0.9835 0.5939 0.9568 0.7055 0.7783 0.9835 0.9760 0.4861 0.9899 0.7313 0.7822 0.9678 0.9503 0.5481 0.9694 0.6095

0.9636 0.9992 0.9983 0.9934 0.9993 0.8273 0.9738 0.9987 0.9968 0.9967 0.9995 0.8534 0.9757 0.9988 0.9957 0.9962 0.9989 0.8141

MRA

CT

Table 7 MSE, PSNR, SSIM and NC of different attacks for rbio5.5. Type of attack

Image

MSE

PSNR

SSIM

NC

Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack

X-ray

0.0044 0.00009 0.0002 0.0007 0.0003 0.0201 0.0046 0.0002 0.0005 0.0006 0.0002 0.0237 0.0043 0.0002 0.0007 0.0006 0.0004 0.0303

55.1448 94.0518 86.0641 72.3635 79.7702 40.0091 54.3561 84.8380 75.9769 73.9852 82.6467 37.8980 55.2079 85.8316 73.2729 73.3857 78.5758 35.6941

0.7569 0.9892 0.9835 0.5939 0.9568 0.7055 0.7665 0.9835 0.9760 0.4836 0.9899 0.7313 0.7756 0.9678 0.9503 0.5459 0.9697 0.6095

0.9636 0.9992 0.9983 0.9934 0.9993 0.8274 0.9726 0.9987 0.9968 0.9967 0.9995 0.8534 0.9742 0.9988 0.9957 0.9961 0.9989 0.8141

MRA

CT

Table 8 MSE, PSNR, SSIM and NC of different attacks for dmey. Type of attack

Image

MSE

PSNR

SSIM

NC

Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack

X-ray

0.0046 0.00009 0.0002 0.0007 0.0004 0.0201 0.0045 0.0002 0.0005 0.0006 0.0002 0.0237 0.0042 0.0002 0.0007 0.0007 0.0004 0.0303

54.6505 94.0543 86.0669 72.3297 79.7702 40.0083 54.5935 84.8371 75.9764 74.0751 82.6460 37.8982 55.5299 85.8317 73.2724 73.3444 78.5749 35.6943

0.7483 0.9892 0.9835 0.5930 0.9568 0.7055 0.7713 0.9835 0.9760 0.4867 0.9899 0.7313 0.7813 0.9678 0.9503 0.5457 0.9696 0.6095

0.9618 0.9992 0.9983 0.9933 0.9993 0.8273 0.9732 0.9987 0.9968 0.9967 0.9995 0.8534 0.9750 0.9988 0.9957 0.9961 0.9989 0.8141

MRA

CT

11

T.K. Al-Shayea et al. / Measurement 148 (2019) 106813 Table 9 MSE, PSNR, SSIM and NC of different attacks for sym5. Type of attack

Image

MSE

PSNR

SSIM

NC

Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack

X-ray

0.0044 0.00009 0.0002 0.0007 0.0003 0.0201 0.0047 0.0002 0.0005 0.0006 0.0002 0.0237 0.0004 0.0002 0.0007 0.0006 0.0004 0.0303

55.1923 94.0537 86.0654 72.3484 79.7704 40.0089 54.0063 84.8379 75.9772 73.9439 82.6468 37.8981 55.0284 85.8314 73.2727 73.4063 78.5768 35.6943

0.7618 0.9892 0.9835 0.5929 0.9568 0.7055 0.7600 0.9835 0.9760 0.4841 0.9899 0.7313 0.7703 0.9678 0.9503 0.5471 0.9696 0.6095

0.9637 0.9992 0.9983 0.9933 0.9993 0.8274 0.9716 0.9987 0.9968 0.9966 0.9995 0.8534 0.9738 0.9988 0.9957 0.9962 0.9989 0.8141

MRA

CT

Table 10 MSE, PSNR, SSIM and NC of different attacks for sym8. Type of attack

Image

MSE

PSNR

SSIM

NC

Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack

X-ray

0.0045 0.00009 0.0002 0.0007 0.0003 0.0201 0.0045 0.0002 0.0005 0.0006 0.0002 0.0237 0.0042 0.0002 0.0007 0.0007 0.0004 0.0303

54.8912 94.0530 86.0653 72.3705 79.7706 40.0086 54.5935 84.8368 75.9757 74.0754 82.6477 37.8983 55.5299 85.8316 73.2723 73.3452 78.5757 35.6942

0.7521 0.9892 0.9835 0.5942 0.9568 0.7055 0.7713 0.9835 0.9760 0.4867 0.9899 0.7313 0.7813 0.9678 0.9503 0.5458 0.9697 0.6095

0.9627 0.9992 0.9983 0.9934 0.9993 0.8273 0.9732 0.9987 0.9968 0.9967 0.9995 0.8534 0.9750 0.9988 0.9957 0.9961 0.9989 0.8141

MRA

CT

Table 11 MSE, PSNR, SSIM and NC of different attacks for coiflets4. Type of attack

Image

MSE

PSNR

SSIM

NC

Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack

X-ray

0.0045 0.00009 0.0002 0.0007 0.0003 0.0201 0.0044 0.0002 0.0005 0.0006 0.0002 0.0237 0.0043 0.0002 0.0007 0.0006 0.0004 0.0303

54.9794 94.0548 86.0669 72.3275 79.7706 40.0082 54.8526 84.8361 75.9748 74.0262 82.6495 37.8993 55.2079 85.8319 73.2713 73.3849 78.5777 35.6948

0.7559 0.9892 0.9835 0.5928 0.9568 0.7055 0.7783 0.9835 0.9760 0.4861 0.9899 0.7313 0.7756 0.9678 0.9503 0.5459 0.9695 0.6095

0.9630 0.9992 0.9983 0.9933 0.9993 0.8273 0.9738 0.9987 0.9968 0.9967 0.9995 0.8534 0.9742 0.9988 0.9957 0.9961 0.9989 0.8141

MRA

CT

12

T.K. Al-Shayea et al. / Measurement 148 (2019) 106813

Table 12 MSE, PSNR, SSIM and NC of different attacks for coiflets5. Type of attack

Image

MSE

PSNR

SSIM

NC

Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack

X-ray

0.0045 0.00009 0.0002 0.0007 0.0003 0.0201 0.0044 0.0002 0.0005 0.0006 0.0002 0.0237 0.0042 0.0002 0.0007 0.0007 0.0004 0.0303

54.9445 94.0543 86.0668 72.2849 79.7705 40.0079 54.7947 84.8350 75.9734 73.9618 82.6501 37.8995 55.3938 85.8319 73.2709 73.3597 78.5771 35.6948

0.7527 0.9892 0.9835 0.5921 0.9568 0.7055 0.7775 0.9835 0.9760 0.4835 0.9899 0.7313 0.7788 0.9678 0.9503 0.5464 0.9695 0.6095

0.9628 0.9992 0.9983 0.9933 0.9993 0.8273 0.9737 0.9987 0.9968 0.9967 0.9995 0.8534 0.9747 0.9988 0.9957 0.9961 0.9989 0.8141

MRA

CT

Table 13 MSE, PSNR, SSIM and NC of different attacks between dmey, rbio6.8 and coifletS5. Type of attack

Image

MSE

PSNR

SSIM

NC

Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack

X-ray

0.0045 0.00009 0.0002 0.0007 0.0003 0.0201 0.0044 0.0002 0.0005 0.0006 0.0002 0.0237 0.0041 0.0002 0.0007 0.0006 0.0004 0.0303

54.9445 94.0536 86.0658 72.2856 79.7705 40.0086 54.7947 84.8373 75.9766 73.9633 82.6470 37.8982 55.7886 85.8315 73.2725 73.4201 78.5758 35.6942

0.7527 0.9892 0.9835 0.5921 0.9568 0.7055 0.7776 0.9835 0.9760 0.4836 0.9899 0.7313 0.7822 0.9678 0.9503 0.5481 0.9696 0.6095

0.9628 0.9992 0.9983 0.9933 0.9993 0.8273 0.9737 0.9987 0.9968 0.9967 0.9995 0.8534 0.9757 0.9988 0.9957 0.9962 0.9989 0.8141

MRA

CT

Table 14 MSE, PSNR, SSIM and NC of different attacks between bior3.5, bior5.5 and rbiO6.8. Type of attack

Image

MSE

PSNR

SSIM

NC

Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack Noise attack (Salt and Pepper) Median attack Mean attack Adjust image attack Rotation attack

X-ray

0.0043 0.00009 0.0002 0.0007 0.0003 0.0201 0.0046 0.0002 0.0005 0.0006 0.0002 0.0237 0.0044 0.0002 0.0007 0.0004 0.0303

55.3871 94.0539 86.0655 72.3400 79.7706 40.0088 54.3240 84.8378 75.9771 74.0477 82.6470 37.8981 55.0284 85.8315 73.2727 78.5764 35.6942

0.7632 0.9892 0.9835 0.5934 0.9568 0.7055 0.7676 0.9835 0.9760 0.4858 0.9899 0.7313 0.7703 0.9678 0.9503 0.9696 0.6095

0.9643 0.9992 0.9983 0.9933 0.9993 0.8274 0.9725 0.9987 0.9968 0.9967 0.9995 0.8534 0.9738 0.9988 0.9957 0.9989 0.8141

MRA

CT

13

T.K. Al-Shayea et al. / Measurement 148 (2019) 106813 Table 15 MSE, PSNR, SSIM and NC of different attacks between bior3.5, bior5.5 and sym5. Type of attack

Image

MSE

PSNR

SSIM

NC

Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack Noise attack (Salt and Pepper) Median attack Mean attack Gaussian noise attack Adjust image attack Rotation attack

X-ray

0.0044 0.00009 0.0002 0.0007 0.0003 0.0201 0.0045 0.0002 0.0005 0.0006 0.0002 0.0237 0.0046 0.0002 0.0007 0.0007 0.0004 0.0303

55.1923 94.0559 86.0677 72.3482 79.7710 40.0082 54.5935 84.8375 75.9760 74.0750 82.6490 37.8990 54.4434 85.8322 73.2717 73.3174 78.5780 35.6948

0.7618 0.9892 0.9835 0.5929 0.9568 0.7055 0.7713 0.9835 0.9760 0.4867 0.9899 0.7313 0.7599 0.9678 0.9503 0.5453 0.9695 0.6095

0.9637 0.9992 0.9983 0.993 0.9993 0.8273 0.9732 0.9987 0.9968 0.9967 0.9995 0.8534 0.9722 0.9988 0.9957 0.9961 0.9989 0.8141

MRA

CT

where W indicates the embedded watermarking and W0 indicates the extracted watermarking. Increasing the NC value denotes more similarity between the host image and the watermarked image. Structural Similarity Index (SSIM) is utilized for measuring image quality. SSIM quality estimation index is based on the computation of three terms. These are the luminance term, the contrast term, and the structural term. The overall index is a multiplicative combination of the three terms [44].

SSIMðx; yÞ ¼ ½Iðx; yÞa :½C ðx; yÞb :½Sðx; yÞc

ð8Þ

where

Iðx; yÞ ¼

2lx ly þ C 1 2r x r y þ C 2 rxy þ C 3 ; C ðx; yÞ ¼ 2 2 ; Sðx; yÞ ¼ l2x þ l2y þ C 1 rx ry þ C 2 rx ry þ C 3

where lx, ly, rx, ry, and rxy are the local means, standard deviations, and cross-covariance for images , y. If a = b = c = 1 (the default for Exponents), and C3 = C2/2 (default selection of C3) the index simplifies to:

SSIMðx; yÞ ¼

ð2lx ly þ C 1 Þð2rxy þ C 2 Þ ðl2x þ l2y þ C 1 Þðr2x þ r2y þ C 2 Þ

ð9Þ

The statistical parameters are the most frequently watermarked image quality metric used by the previous research studies. Four statistical parameters are utilized in this study to determine the levels of strength and weakness of watermarking algorithms. There is therefore a need to use PSNR, MSE, SSIM, and NC measures for assessing the robustness and imperceptibility of the designed medical images. To evaluate the robustness of the used medical image, it is necessary to implement multiple processing attacks, in order to distort the watermarked image and compare the image before and after the action is taken. We are looking to minimize the MSE and maximize the PSNR, SSIM, and NC. Table 1 shows the MSE, PSNR, SSIM, and NC with no attacks to evaluate the performance of the proposed watermarking algorithms. Three different medical image types are used as a host image (X-ray image, MRA image, and CT image) as shown in Fig. 1. The proposed wavelet families transform and the proposed hybridization between wavelet families retains the image’s quality after processing and without distorting the medical image as illustrated in Table 1. Tables 2–12 show the MSE, PSNR, SSIM, and NC with different attacks for Bior6.8, Bior3.5, Bior5.5, Rbio6.8, Rbio3.5, Rbio5.5, Dmey, Sym5, Sym8, Coif4, and Coif5 wavelet transform respectively. While Tables 13–15 show the MSE, PSNR, SSIM, and NC with different attacks for hybridization wavelet transform.

When looking at all the tables, there is a little difference between the results for the proposed watermarking algorithms with no attack and those with various attacks. The results are promising in that even after attack on the watermarked image takes place, the robustness of the proposed watermark algorithms is still shows potential. MSE, PSNR, SSIM, and NC are used to measure the performance of the proposed watermark algorithms as illustrated in the tables. Our proposed algorithm is of significant importance, compared with other previous work, as reflected in the results of the study. It is important to highlight the role of watermarking, with respect to contemporary medical information security and authenticity. The proposed paper proves that watermarking has found a significant role in the healthcare sector, as a tool for secure sharing and handling of medical digital images, and for the securing of medical information. The preservation of information associated with medical reports and digital images and prevention from distortion and loss of information will maintain diagnostic integrity. This work also utilizes the IoT, which is one of the most pertinent technical challenges of this era. 6. Conclusion This paper proposes the introduction of a watermarking medical scheme healthcare IoT infrastructure that uses different wavelet transform families. These are (biorthogonal6.8, biorthogonal3.5, biorthogonal5.5, reverse biorthogonal6.8, reverse biorthogonal3.5, reverse biorthogonal5.5, discrete meyer, symlets5, symlets8 coiflets4 wavelet, and coiflets 5). Furthermore, hybridization between wavelet families (discrete meyer, reverse biorthogonal6.8 and coiflets5), (biorthogonal3.5, biorthogonal5.5 and reverse biorthogonal6.8), and (biorthogonal 3.5, biorthogonal5.5 and symlets5), which are resistant against various sorts of attacks, interdicts the manipulation and authentication of medical images. We have implemented three types of medical image to employ the proposed watermarking algorithm. The embedding of the watermark is considered a fundamental technique for individualizing the medical images by inserting reliability and identification into clinical information. PSNR, MSE, SSIM, and NC are utilized to evaluate the performance of the proposed algorithm. Several attacks are enforced to apprais the durability of the proposed watermarking algorithm. There are few obvious differences between the results for each of the medical images. The experimental results are deemed promising amounts when compared with results obtained from other algorithms that had been proposed in the previous literature. In future work, four level decomposition of DWT will be implemented with different attacks.

14

T.K. Al-Shayea et al. / Measurement 148 (2019) 106813

Acknowledgments The research work presented in this article is part of the Ambient Assisted Living (AAL) project vINCI: ‘‘Clinically-validated INtegrated Support for Assistive Care and Lifestyle Improvement: the Human Link” funded by Research Promotion Foundation in Cyprus under the AAL framework with Grant Nr. vINCI /P2P/ AAL/0217/0016 as well as part of the Celtic+ Programme, under the project 5G Perfecta: 5G and next generation mobile Performance compliance testing assurance by the Polish National Centre for Research and Development. References [1] M. Elhoseny, G. Ramirez-Gonzalez, Y. Yang, O.M. Abu-Elnasr, S.A. Shawkat, N. Arunkumar, A. Farouk, Secure medical data transmission model for IoT-based healthcare systems, IEEE Access 6 (2018) 20596–20608. [2] S.P. Mohanty, E. Kougianos, P. Guturu, SBPG: secure better portable graphics for trustworthy media communications in the IoT, IEEE Access 6 (2018) 5939– 5953. [3] S. Nataraja, P. Nataraja, IoT based application for E-health an improvisation for lateral rotation, 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), 2017. [4] K. Shekaramiz, A. Naghsh, Embedding and extracting two separate images signal in salt & pepper noises in digital images based on watermarking, 2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA), 2017. [5] A. Ghai, A. Kansal, Hariom, Protection against various attacks by using Semi fragile technique, 2017 3rd International Conference on Advances in Computing, Communication & Automation (ICACCA) (Fall), 2017. [6] M. Andalibi, D.M. Chandler, Digital image watermarking via adaptive logo texturization, IEEE Trans. Image Process. 24 (12) (2015) 5060–5073. [7] A.V. Subramanyam, S. Emmanuel, M.S. Kakanhalli, Robust watermarking of compressed and encrypted JPEG2000, IEEE Trans. Multimedia 14 (3) (2017) 703–716, Images. [8] E. Nezhadarya, Z.J. Wang, R.K. Ward, Robust image watermarking based on multiscale gradient direction quantization, IEEE Trans. Inf. Forensics Secur. 6 (4) (Dec. 2011) 1200–1213. [9] S. Sarreshtedari, M.A. Akhaee, A. Abbasfar, A watermarking method for digital speech self-recovery, IEEE/ACM Trans. Audio, Speech, Lang. Process 23 (11) (. 2015) 1917–1925. [10] X. Wang, W. QI, P. Niu, A new adaptive digital audio watermarking based on support vector regression, IEEE Trans. Audio, Speech, Lang. Process. 15 (8) (2007) 2270–2277. [11] H. Khalilian, I.V. Bajic, Video watermarking with empirical PCA based decoding, IEEE Trans. Image Process. 22 (12) (2013) 4825–4840. [12] M. Asikuzzaman, M.J. Alam, A.J. Lambert, M.R. Pickering, Robust DT CWT-based DIBR 3D video watermarking using chrominance embedding, IEEE Trans. Multimedia 18 (9) (2016) 1733–1748. [13] J. Zhang, A.T.S. Ho, G. Qiu, P. Marziliano, Robust video watermarking of H.264/ AVC, IEEE Trans. Circuits Syst. -II: Express Briefs 54 (2) (2007) 205–209. [14] K. Rana, S. Thakur, Comparisons of wavelets and algorithms based on wavelets and comparing the results with JPEG, 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), 2017. [15] A. Singh, M. Dutta, Lossless and robust digital watermarking scheme for retinal images, 2018 4th International Conference on Computational Intelligence & Communication Technology (CICT), 2018. [16] A. Al-Haj, H. Abdel-Nabi, Digital image security hiding and cryptography, 2017 3rd International Conference on Information Management (ICIM), 2017. [17] A. Shehab, M. Elhoseny, K. Muhammad, A.K. Sangaiah, P. Yang, H. Huang, G. Hou, Secure and robust fragile watermarking scheme for medical images, IEEE Access 6 (2018) 10269–10278. [18] R. Liu, T. Tan, An SVD-based watermarking scheme for protecting rightful ownership, IEEE Trans. Multimedia 4 (1) (2002) 121–128. [19] R. Rykaczewski, Comments on ‘‘An SVD-Based Watermarking Scheme for Protecting Rightful Ownership”, IEEE Trans. Multimedia 9 (2) (Feb. 2007) 421– 423. [20] X. Zhang, K. Li, Comments on ‘‘An SVD-based watermarking scheme for protecting rightful ownership”, IEEE Trans. Multimedia 7 (2) (2005) 593–594. [21] S. Dong, J. Li, S. Liu, Frequency Domain Digital Watermark Algorithm Implemented in Spatial Domain Based on Correlation Coefficient and Quadratic DCT Transform, pp. 596–600.

[22] N.M. Makbol, B.E. Khoo, T.H. Rassem, Block-based discrete wavelet transform singular value decomposition image watermarking scheme using human visual system characteristics, IET Image Process. 10 (1) (2016) 34–52. [23] S. Tyagi, H.V. Singh, Image watermarking using genetic algorithm in DCT domain, International Conference on Inventive Systems and Control (ICISC2017), 2017. [24] K. Ramanjaneyulu, K. Rajarajeswari, ‘‘Wavelet-based oblivious image watermarking scheme using genetic algorithm, IET Image Process. 6 (4) (2012) 364–373. [25] S. Liu, Z. Pan, H. Song, Digital image watermarking method based on DCT and fractal encoding, IET Image Process. 11 (10) (2017) 815–821. [26] M. Veni, T. Meyyappan, DWT DCT based new image watermarking algorithm to improve the imperceptibility and robustness, International Conference on Electrical, Instrumentation and Communication Engineering (ICEICE2017), 2017. [27] S. Anu, H. Nair, P. Aruna, Comparison of DCT, SVD and BFOA based multimodal biometric watermarking systems, Alexandria Eng. J. 54 (2015) 1161–1174. [28] T. Bhuyan, V.K. Srivastava, F. Thakkar, Shuffled SVD based robust and secure digital image watermarking, International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), 2016. [29] V. Verma, V.K. Srivastava, F. Thakkar, DWT-SVD based digital image watermarking using swarm intelligence, International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), 2016. [30] B. Usmonov, O. Evsutin, A. Iskhakov, A. Shelupanov, A. Iskhakova, R. Meshcheryakov, The cybersecurity in development of IoT embedded technologies, 2017 International Conference on Information Science and Communications Technologies (ICISCT), 2017. [31] K. Sarwar, S. Yongchareon, J. Yu, Lightweight ECC with fragile zerowatermarking for internet of things security, 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE), 2018. [32] A. Ferdowsi, W. Saad, Deep learning-based dynamic watermarking for secure signal authentication in the internet of things, 2018 IEEE International Conference on Communications (ICC), 2018. [33] G. Singh, A review of secure medical image watermarking, 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), 2017. [34] L. Ko, J. Chen, Y. Shieh, T. Sung, A novel fractional discrete cosine transform based reversible watermarking for biomedical image applications, 2012 International Symposium on Computer, Consumer and Control, 2012. [35] S. Mehta, R. Nallusamy, R.V. Marawar, B. Prabhakaran, A study of DWT and SVD based watermarking algorithms for patient privacy in medical images, 2013 IEEE International Conference on Healthcare Informatics, 2013. [36] P. Aparna, P.V.V. Kishore, Biometric-based efficient medical image watermarking in E-healthcare application, IET Image Process. 13 (3) (2019) 421–428. [37] Z. Ali, M.S. Hossain, G. Muhammad, M. Aslam, New zero-watermarking algorithm using hurst exponent for protection of privacy in telemedicine, IEEE Access 6 (2018) 7930–7940. [38] J. Singh, A.K. Patel, An effective telemedicine security using wavelet based watermarking, 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 2016. [39] H.M. Hazzaa, S.K. Ahmed, Watermarking algorithm for medical images authentication, 2015 4th International Conference on Advanced Computer Science Applications and Technologies (ACSAT), 2015. [40] A. Al-Haj, N. Hussein, G. Abandah, Combining cryptography and digital watermarking for secured transmission of medical images, 2016 2nd International Conference on Information Management (ICIM), 2016. [41] F.Y. Shih, Digital Watermarking and Steganography Fundamentals and Techniques, CRC Press Taylor and Francis Group, 2017. [42] T.K. Al-Shayea, C.X. Mavromoustakis, J.M. Batalla, G. Mastorakis, E.K. Markakis, E. Pallis, On the efficiency evaluation of a novel scheme based on daubechies wavelet for watermarking in 5G, IEEE 23rd International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), 2018. [43] S. Mallat, A Wavelet Tour of Signal Processing the Sparse Way, Elsevier Inc., 2009. [44] https://www.mathworks.com/help/images/ref/ssim.html. [45] S. Liu, Z. Pan, H. Song, Digital image watermarking method based on DCT and fractal encoding, IET Image Process. 11 (2017) 815–821. [46] R. Gao, R. Yan, ‘‘Discrete Wavelet Transform”, Wavelets Theory and Applications for Manufacturing, Springer, 2011. [47] http://wavelets.pybytes.com/ (last accessed on 30th of July 2018).