An optimized blind dual medical image watermarking framework for tamper localization and content authentication in secured telemedicine

Biomedical Signal Processing and Control 55 (2020) 101665

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Biomedical Signal Processing and Control journal homepage: www.elsevier.com/locate/bspc

An optimized blind dual medical image watermarking framework for tamper localization and content authentication in secured telemedicine Swaraja K ∗ , Meenakshi K, Padmavathi Kora GRIET, India

a r t i c l e

i n f o

Article history: Received 25 March 2019 Received in revised form 16 July 2019 Accepted 17 August 2019 Keywords: Human Visual System Entropy Security Authenticity Schur transform Tamper detection ROI curves Telemedicine

a b s t r a c t Maintaining secured patient credentials in telemedicine is becoming a critical task. Image watermarking is one of the solutions to this problem. It is extensively used to protect and block the content alteration. Medical images may acquaint with tampers during transit in telemedicine. Before taking a prior decision about referring for diagnosis, the reliability of region of interest (ROI) of the watermarked medical test image must be tested to avoid faulty diagnosis. In this paper, tamper recognition and authenticity were obtained by concealing the dual watermarks into the region of non-interest (RONI) blocks of the medical image. These blocks are chosen by the characteristics of Human Visual System (HVS) with the integration of Discrete Wavelet Transform (DWT) and Schur transform along with the Particle Swarm Bacterial Foraging Optimization algorithm (PSBFO). The major focus of the PSBFO algorithm is to select the threshold value for obtaining optimum results in terms of imperceptibility and robustness against attacks. The dual watermarks are compressed by Lempel-Ziv-Welch (LZW) lossless compression algorithm to increase the payload capacity. Simulation outcomes conducted on different types of medical images disclose that the proposed scheme demonstrates superior transparency and robustness against signal and compression attacks compared with the related hybrid optimized algorithms. It also recognizes the existence of tampers inside the portion of ROI with 100% precision. The proposed scheme is also able to retrieve the original ROI without losing any information and provides optimum security capability when compared with the state-of-the-art algorithms. © 2019 Elsevier Ltd. All rights reserved.

1. Introduction Telemedicine is a rapidly developing application of clinical medicine where medical information is transferred through interactive audiovisual media for the purpose of consulting, and sometimes for remote medical procedures or examinations. Telemedicine is a computer networks-built health care facility to assist the individuals in remote regions of the country and may be as simple as two health professionals discussing a case over the telephone, or as complex as using satellite technology and video conferencing equipment to conduct a real-time consultation between medical specialists in two different countries. Medical image watermarking is a subset of image watermarking [1] whereby medical images are embedded with hidden information that may be used to assert ownership, increase the security, and

∗ Corresponding author. E-mail address: [email protected] (S. K). https://doi.org/10.1016/j.bspc.2019.101665 1746-8094/© 2019 Elsevier Ltd. All rights reserved.

verify the numerical integrity of medical images. Tele-radiology [2] is a significant division of telemedicine employed to transfer medical images from one locality to another for providing medical expertise. Initially, because of the noises generated through the communication channel and various operations of hackers, digital communication technology is unreliable for safe on line image communication. As a result, security of the medical image has been crucial where both the use and cost of collecting data makes any loss undesirable. Even though various data concealing schemes are formulated in medical image safety of teleradiology application, image watermarking techniques are used to safeguard the confidentiality of patients. The bandwidth of proposed watermarking scheme is reduced by concealing dual watermarks-one comprising Electronic Patient Record (EPR) and another is the ROI to avoid any type of tampering for preventing wrong diagnosis. The medical image consists of two portions termed as ROI and RONI. From the perspective of diagnosis, ROI portion is significant and should be free from any type of distortion. Even though ROI is tampered, it is required to reproduce

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original ROI at the receiving end. From medical point of view RONI [3–10] is insignificant and is used for concealing the side information. Once the tamper is identified within the region of interest of watermarked medical image, then the region with tamper in ROI is exchanged with the retrieved data concealed within the RONI. In wide ranging watermarking system, optimization methods are essential to ensure the trade-off towards the constraints of watermarking system such as robustness and imperceptibility along with payload capacity. At present, Artificial Intelligence (AI) schemes identified as Genetic Algorithm (GA), Neural Networks (NN), Differential Evolution (DE) and Particle Swarm Optimization (PSO) [11–16] are utilized as optimization methods to explore optimal sub-bands and coefficients in transform domain to conceal watermark with distinct scaling factors. Keeping this in view, in this work, a novel blind hybrid optimized region based robust medical image watermarking algorithm is planned through implementing the DWT, Schur, HVS characteristics along with PSBFO algorithm to attain the optimum values with regard to the constraints of watermarking system. In addition to this, issues related with security and tamper localization capabilities are taken care in the proposed scheme by recognizing the tampers inside the ROI. It also can retrieve the original ROI without any loss, through mapping the ROI pixels concealed as one of the watermarks in the RONI portion with the tampered pixels inside the ROI at the receiver end. At the same time there is no chance of degradation in ROI as no data is concealed in the ROI portion. The other watermark which includes the EPR is concealed into the RONI portion to provide authenticity. The remaining part of the work is planned into six sections. Section 2 describes the state of art in current medical image watermarking. Section 3 suggests a brief overview of entropy, DWT, Schur transform and PSBFO technique. In Section 4, blind hybrid optimized medical image watermarking concealing and extraction is formulated. Simulation results are conveyed in Section 5 about the various constraints of the watermarking algorithm. Lastly, the conclusion is stated in Section 6.

2. Related work In this section, a detailed analysis of the literature is described on the recent contemporary medical as well as digital image watermarking schemes utilizing multiple watermarking methods [17,18], hybrid and region based techniques with tamper localization functionality [19,20] and optimization techniques [21–23]. A new robust hybrid dual watermarking algorithm is presented in [17] through combining the Discrete Wavelet Transforms (DWT), discrete cosine transforms (DCT), and singular value decomposition (SVD) rather than individually executing each transform or different combination of DWT-SVD/DCT-SVD. Aimed at individuality authentication, dual watermarks were concealed into the same medical test image concurrently to offer more security with satisfactory results allied to robustness and imperceptibility. Hybrid multiple watermarking scheme is presented by [18] to conceal multiple watermarks in the test image through exploiting the amalgamation of DWT, discrete cosine trans-forms (DCT) besides singular value decomposition (SVD) rather than applying DWT, DCT and SVD separately. In region based watermarking schemes, the medical images are segregated into portions such as: Region-ofInterest (ROI) in addition to Region-of Non-interest (RONI). In the literature some of the watermarking schemes for medical images related to region with tamper localization capability has been presented and few of them are listed here. In Parah et al. [19] two distinct image watermarking methods for Medical images are performed by exploiting DCT, in the initial method, watermark is concealed in the whole test image while in the subsequent method the medical image is alienated into ROI besides RONI. ROI diagnosed

or identified by the physician and is segregated from the RONI. The watermark bits are concealed only within the RONI area of the medical image undergone for testing. The watermarked portion in the RONI of test image is aggregated with ROI portion after concealing the watermark. The assessment of robustness is performed manually by selecting multiple scaling factors. In the scheme presented by [20], the medical image to be tested is alienated into three groups of pixels: pixels with ROI portion, pixels with region of noninterest (RONI) portion, besides pixels with border portion. Subsequently, the information related to authentication of ROI portion pixels are concealed within the pixels of border portion. Retrieval data of ROI is concealed into the portion of RONI. There is no change in the portion of ROI pixels of watermarked medical image as data is not concealed inside the pixels of ROI portion. By calculating the average and variance values of blocks, the scheme precisely recognizes and limits the blocks tampered inside the portion of ROI. Pixels in the ROI portion are retrieved without damage as the pixels in tampered blocks are exchanged with original values of the pixel. From the past three decades, evolutionary procedures such as Genetic Algorithm, PSO, DE, Fuzzy logic, Neural Networks in addition to Support Vector Machine are inspiring and performing a substantial role in many existing lifetime applications, together with medical image watermarking for boosting the evaluation with regard to optimal constraints. In [21] horizontal third-and second-level sub-band (LH2 and LH3) coefficients were assembled into dissimilar blocks, such that every single block must comprise single coefficient from LH3 sub-band plus four coefficients 2 × 2 non-overlapping block) from LH2 sub-band. These coefficients are utilized to calculate a distance vector. While extracting the watermark, a threshold-centered statistical decoder is planned. The GA in this scheme is exploited for optimization of constraints. This method can efficiently conceal upto 2048 watermark bits without degrading the quality (NCC) of the extracted watermark image. A PSO method is utilized in watermarking medical images to augment the payload of the watermark along with boosting the quality of watermarked medical image of the scheme. A reversible watermarking scheme by exploiting DWT-SVD together with DE algorithm on medical images (such as CT scan and X-ray) is presented in [22]. The algorithm does not consider the false positive problem present in SVD. It is avoided in the proposed work by generating image hash at transmitting and receiving end. Through simulation results, it is proved that the method attains more stability between payload capacity, robustness towards attacks and good quality of the test image. PSO utilized in [23], proposes two schemes for augmenting the consistency and robustness. The consistency of the first scheme is developed by concealing the principal components of the watermark into the test image through transformed discrete cosine coefficients while in the other scheme; those are inserted into the test image in transformed coefficients of discrete wavelets. To augment the robustness, the PSO is exploited in estimating the apt scaling factors. The second scheme with PSO offers improved correlation coefficient as well as PSNR under numerous attacks than the first method. In the literature while assessing with existing algorithms, only a few medical image watermarking methods related to region have been revealed with tamper localization capability. Thus in this work a novel scheme is proposed, which provides many such features as authentication, tamper detection and confidentiality for copyright protection with optimum results. The proposed method conceals dual watermark bits (ROI watermark besides EPR watermark) into the blind hybrid optimized watermarking system initially through transforming the pixels of the test image by DWT then LH and HL sub-bands are chosen and among them the best blocks are selected by utilizing the HVS characteristics rather than deciding the blocks arbitrarily which may prone to the degradation in the quality of the watermarked image as well as robustness. Sub-

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3

sequently Schur transform is implemented on the selected block coefficients of the medical image taken for testing while concealing the ROI watermark and EPR watermark. PSBFO algorithm is exploited in the planned work to opt the threshold value “T” while concealing the multiple robust watermarks into the test image, to obtain the optimum results with regard to robustness to attacks, imperceptibility, authentication, confidentiality and security of the watermarked image. The main focus and target of the PSBFO algorithm is to attain optimum results with reference to the quality of the watermarked image besides robustness, as they are the main contradictory demands with the system of watermarking along with the security issues. In Section 6 the simulation outcome also supports the capability of the PSBFO-centered blind hybrid watermarking scheme, since it is extremely strong counter to selected common attacks comprising checkmark attacks which can simultaneously enhance the robustness, capacity of the watermark and imperceptibility. 3. Materials and methods This watermarking scheme conceals watermark in selected blocks based on visual entropy and edge entropy. 3.1. Entropy In information theory [24] Entropy gives the amount of randomness. In image, the watermark concealed in textural regions is difficult to perceive. So, the texture of image is determined using visual entropy defined n 

E1 = −

(1)

Where pi is the probability of occurrence of the event and sum of probabilities of all the events should be 1. Therefore, the E1 is observed as a global measure pertaining to the image. In [25] it is revealed that the entropy function in an exponential form can acquire two dimensional (2D) spatial relationship of image in a superior way when assessed with Shannon’s entropy. The visual entropy relies mainly on Probability Distribution of the image intensities. This entropy does not consider co-occurrence matrix into account. To address this, edge entropy is used in addition to visual entropy and it is defined as below:



as it is a well-organized mathematical tool as it requires n3 flops for an n × n matrix where as SVD require 11n3 flops. Thus the amount of calculations carried out in Schur is below one third of those executed in SVD [27]. There are two categories of Schur decomposition: complex Schur in addition to real Schur. The real Schur decomposes image into two matrices – Unitary matrix (U) and Upper Traingular matrix S.



pi log pi

i=1

n

E2 = −

Fig. 1. Two level DWT of medical image.

pi exp1−pi

(2)

i=1

3.2. Discrete Wavelet Transform The anisotropic and multiresolution properties of DWT is utilized in the work to develop a robust and imperceptible design. It is also difficult for hacker to extract watermark from middle frequency bands as watermark concealed in LL band is more robust to attacks and watermark concealed in HH band is more imperceptible. Compared to LL and HH bands, the middle frequency bands LH and HL bands are more robust to challenging attacks like low quality JPEG compression and rotation. Hence we utilize LH and HL bands for watermark concealing in the proposed work. The 2 level decomposition of DWT on medical image is shown in Fig. 1. 3.3. Schur decomposition The decomposition using Schur is a significant mathematical tool in linear algebra invented by Issai Schur. Indeed, decomposition of Schur correspond to intermediary phase in SVD [26]; as well

Schur(F) = U × S × U Th



(3)

UTh specifies the conjugate transpose of U. The S matrix contains the largest Eigen value of F. 3.4. Bacterial foraging Particle Swarm Optimization based watermarking scheme The proposed watermarking scheme uses a hybrid optimization algorithm combining the good points in both the Particle Swarm Optimization (PSO) and Bacterial Foraging Optimization (BFO). 3.4.1. Particle Swarm Optimization (PSO) Eberhart plus Kennedy developed swarm based optimization method, a kind of PSO [28] which is motivated from the behavior of birds flock. In the search vicinity all particles flies in the group with a rapidity and strives to achieve the optimum velocity compared to its individual earlier finest (lbest ) besides its acquaintances best (abest ) flying knowledge. Every particle in the search area seeks to amend its site using latest site (x) and velocities (v), discrepancy among the latest site and lbest as well as discrepancy among latest site and abest . Every particle accelerating in search area in the path of the abest and the lbest values, with the arbitrary stepping up at every step is the central proposal at the back of PSO [29]. Simplicity is the benefit of utilizing PSO over other optimization methods. Moreover hardly any parameters required to be adapted. In consequence of this, PSO has been extensively practiced in series of applications. k-dimensional exploration space, Xj = (x1 , x2 , x3 , . . ., xk ) permit the particle to reset with locations Pj and velocities Lj then the fitness is evaluated depending on the coordinates position of particles as the input values. Subsequently the movement of particles into novel places is as Eq. (4), (5).







Lj (j + 1) = ωLj (j) + C1 1 lbest Pj (j) + C2 .2 . abest Pj (j) Pj (j + 1) = Pj (j) + Lj (j + 1)



(4) (5)

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3.4.2. Bacterial Foraging Optimization (BFO) At the time of 2002, the investigator [30] build up BFO which involves in a selection process that eliminates the bacterium with poor search schemes. Quite a lot of generations with deprived foraging schemes are destroyed but merely the organisms with fine search approaches stay alive suggesting the “endurance of the precise”. The movement of Bacterial foraging of bacteria “E. coli” [31] is applied as the motivation for embedding (optimizing) the watermark into image. Proper selection of a block for inserting the watermark could be a global optimization problem in image watermarking, that moderates the quantity of unnecessary blocks which are redundant, this guide to acceptable precision. The movement of Bacteria is random in behavior to locate rising nutrients. So, as soon as gradient of cost function is unidentified this optimization method is helpful. BFO is fine because of its smaller amount of mathematical complications. The BFO is an optimization method which is non-gradient and imitates the exploration method of bacterium called as E. coli. It strives to get the most out of its food consumption per unit interval consumed in its hunt. The three functional stages of all bacterium for each element region are (i) Chemotaxis (ii) Swarming and (iii) Reproduction.

– Chemotaxis: Unsystematic walk of E. coli bacterium behavior can be described with swimming and tumbling. Mostly the bacteria called as E. coli progress in two different behaviors. For a precise range of period it will swim in solitary path next it is going to spill (amend the path). It will vary among these two kinds of process for its complete existence. w(i) correspond to ith bacteria and K, the step size involved inside the unsystematic path is precised through the length of swim. In the chemotaxis progression of the bacteria w(i + 1) possibly will be specified by Eq. (6)

w(i + 1) = w(i) +

k(i)D(i)

(6)

T

D(i) Di

3.4.3. Particle Swarm Bacterial Foraging Optimization algorithm (PSBFO) This section ascertains a hybrid procedure entailing BFO along with PSO procedures. The two fundamental stages entailed in the progress of the proposed work are: (i) Global exploration is monitored by means of the PSO function. (ii) Local exploration done by the BFO (chemotaxis) regulates the solution very well. (iii) Benefits of this grouping are (a) Trapping of the algorithm into the local minimum does not occur. (b) Rapidity of the convergence will be boosted. Due to hybrid amalgamation, PSO carries out a global exploration and creates a proximate prime result very quickly by tracking to a confined exploration through BFO which modifies the result and offers a best possible response of top precision. PSO has an intrinsic drawback of being confined in the local prime but has great confluence rapidity while BFO has the shortcoming of extremely deprived confluence rate however has the capability of not being confined in the local elites. After a precise number of ample swims, the consequential result is accumulated in the plunging order. In the existing method, when enduring a chemotactic phase, all bacterium grows transmuted via PSO function. In this phase, all bacterium is supposed paying attention, headed for the abest site and the local exploration in dissimilar areas is taken attention through the BFO (chemotaxis step) procedure. The foremost intention of PSBFO phase involves in opting the best threshold “T” during concealing the watermark bits into the test image. In all the wrapper techniques practiced, PSBFO interprets optimization difficulty with the techniques of progression and has come out as a capable one.

4. The proposed scheme 4.1. Proper selection of region while inserting the watermark

Where D is a random vector, D ∈ (−1, 1). The virtual chemotactic progress of the bacterium called as E. coli might be observed as an arbitrary hill mounting. – Swarming: In E. coli bacteria swarm activities are empirical akin to numerous other classes, everywhere the intricate and steady spatio-time-based sets are produced in a semisolid medium of nutrient. The E. coli shapes one selves resembling the wandering ring then inspiring in the direction of the nutrient food. The bacteria called as E. coli discharge an attractant after the cells were energized through an uppermost rank of succinctness. This assists them to organize into sets and accordingly they travel in the form of coaxial designs of sets by means of more compactness. In E. coli swarm the cell–cell signaling could be evaluated by the subsequent Rosenbrock function given in Eq. (7) m−1  



100 wi+1 − wi2

2

+ (wi − 1)2

 (7)

i=1

Where m is the dimension of threshold used in watermarking and wi is the ith bacterium. – Reproduction: The detrimental bacteria at last breathe whereas the residual recovered bacteria (those offering larger cost values) asexually segregate into two bacteria; moreover they are positioned in their relevant location. As a result the complete dimension of the bacteria swarms rests unceasing.

Region based medical image watermarking is proposed in this work by exploiting visual and edge entropy of the image. The capacity of hiding the data is decided by the entropy of the test image. The HVS can be utilized not only to estimate the discernibility of watermark when it is concealed in the test image, but also can be practiced to manage the discernibility during the procedure of inserting the watermark. Visual and edge entropy used in works of [32,33] is reutilized in the proposed work to extract region for watermark insertion. The sum of these two entropies calculated for all blocks in image are arranged in ascending order. To obtain better imperceptibility and robustness, blocks having minimum value are chosen for inserting dual watermarks EPR and ROI watermark. The test image F is initially partitioned into two regions namely as ROI and RONI. RONI is the portion in test image that contain subtle information, thus it will provide superior security if the information is concealed in RONI portion of the test image. Therefore, the DWT is applied for segregating RONI portion of the test image and later Schur transform is applied on the selected sub-bands (LH2 and HL2) of the DWT test image. The proper selection of the blocks in these sub-bands is based on HVS characteristics of edge entropy and visual entropy. Additionally, the compressed image of the ROI portion of the test medical image along with the secret key is calculated by Lempel-Ziv-Welch (LZW) algorithm which is lossless coding method prior to the insertion of watermark into RONI portion of the test image to prevent the occurrence of image perceptual degradation when watermarks with heavy payload are concealed within the test image. The procedure of embedding and extraction

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5

Fig. 2. Embedding procedure of dual watermark (EPR and ROI) into MRI test color image.

of multiple watermarks within the RONI portion of the MRI test color image is given in Fig. 2.

4.2. Embedding and extraction procedure of EPR and ROI watermark into RONI

• Based on EPR and YROI watermark bits, u3,1 and u4,1 in Uk,l of selected blocks are modified in Nk,l to u3,1 and u4,1 as given in Eqs. (9) and (10).

If wk,l = 1&

  u3,1 − u4,1 < T

then

• The test color medical image F has three independent color planes Red(R), Green(G)and Blue(B). Generally the luminance component (Y) in YCbCr color space is less affected to modifications compared to chrominance components (Cb, Cr), when watermark is inserted. Hence the RGB color plane is transformed into YCbCr. • Dual watermarks EPR and ROI are concealed in the RONI portion of the cover medical image F. The EPR watermark (wk,l ) is first compressed through lossless LZW algorithm and then watermark bits are arranged in the pattern of one-dimensional bits. The EPR watermark is a gray scale image of resolution 160 × 240 and the ROI watermark is a portion of the color medical image which has a spatial resolution of 128 × 128 × 3. LZW algorithm is applied on ROI portion of individual planes of YROI , CbROI and CrROI of cover image to reduce the redundancy in lossless manner. As lossless compression is reversible, the original ROI watermark can be reconstructed from the compressed one. Similar to EPR watermark, YROI of cover image is converted into one-dimensional bits. • DWT is applied on the RONI portion to obtain LL2 , LH2 , HL2 and HH2 bands. Out of the four, LH2 and HL2 bands are segmented into non overlapping blocks of 4 × 4. Out of them, m × m blocks are chosen for watermark insertion based on visual and edge entropy. • Schur transform is implemented on the selected or suitable blocks Nk,l , to decompose it into two matrices Uk,l and Sk,l as given in Eq. (8)





Uk,l , Sk,l = schur Nk,l



(9)

2

The steps of embedding the watermark are outlined as



  ⎧ T  ⎪ u = sign  + × (u ) 3,1 ⎨ 3,1 2   ⎪ ⎩ u4,1 = sign (u4,1 ) ×  − T

(8)

If wk,l = 0&

  u4,1 − u3,1 < T U3,1 + U4,1

then

  ⎧ T ⎪ ⎨ u3,1 = sign (u3,1 ) ×  − 2   (10) ⎪ ⎩ u4,1 = sign (u4,1 ) ×  + T 2

Where  = 2 In most of the recent watermarking methods the threshold “T” has been modified and tuned manually which is more difficult and not optimal in proper setting of the imperceptibility and robustness constraints of the watermarking. But, in the present scheme the threshold “T” is calculated through the objective function generated by an efficient and powerful PSBFO optimization algorithm which plays a significant role in stabilizing the imperceptibility as well as the robustness constraints in the watermarking system. • Inverse Schur transform is applied to obtain N  k,l  Th Nk,l = Uk,l × Rk,l × Uk,l

(11)

Th is the transpose of U . Where Uk,l k,l • Obtain modified LH and HL . Later apply inverse DWT to obtain Ynew . • Merge with Chrominance components Cb and Cr and convert back into true color image to get the test watermarked color image F .

Since the proposed watermarking scheme is blind, only watermarked image is required for the watermark extraction. The steps of watermark (ROI and EPR) extraction are outlined below as follows:

6

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Fig. 3. Dual watermarks for authentication and tamper recognition. (a) Test color image of leg fracture. (b) Segregation of color image into ROI and RONI portion. (c) EPR watermark.

• The test color medical image is converted from RGB to YCbCr color space. • Extract the luminance of watermarked image (Ywater ) and segregate into two regions namely ROI and RONI portion. • Apply DWT on RONI portion. It segregates into LLwa , LHwa , HLwa and HHwa . • Extract LHwa and HLwa . • Apply Schur transform on selected blocks of N  to obtain the k,l modified unitary matrix uk,l . • The EPR watermark and YROIEXT are extracted from the same position where they were concealed in the watermark embedding algorithm. In it the relation among the entries of the third u3,1  are and the fourth u4,1 rows in the first column of the matrix Uk,l  utilized to extract the watermark bit wk,l as given in Eq. (9)



 wk,l =

1 if 0

u ≥ u 3,1 4,1

(12)

otherwise

• LZW is a reversible compression algorithm. Inverse LZW is applied on the obtained one-dimensional bit streams. • These extracted one-dimensional bit streams are transformed to 2-dimensional array to obtain extracted EPR watermark and extracted portion of YROI . The YROI is merged with CbROI and CrROI and later the YCbCr space is converted into RGB to obtain extracted ROI watermark. 4.3. The role of the PSBFO algorithm in finding the optimal constraints The optimum threshold parameters are obtained by PSBFO algorithm. The watermarking schemes are suffered with the problem of mutually conflicting parameters of imperceptibility and robustness. When T is more, robustness is more, but imperceptibility is poor. When T is less, the contrary is true, the imperceptibility is more and robustness is poor. To reduce the trade-off between these two factors a fitness function based on Peak Signal to Noise Ratio (PSNR) between original and watermarked test color image and Normalized Cross Correlation (NCC) between embedded and extracted watermarks is formulated in Eqs. (14) and (15).

⎛ ⎞ NA  1 fmin = 10 × PSNR − PSNRoptimal + ⎝1 − NC j ⎠ NA

(13)

j=1

The objective function is expressed in mathematical form as specified in Eq. (13). Where NA indicates the total number of attacks subjected on proposed watermarking scheme, NCj is the NCC of extracted watermark corresponding to jth attack, whereas PSNRoptimal is the anticipated value of the PSNR. The adaptation of optimal PSNR corrects the optimization in a way to guarantee the lowest amount of image quality that need to be attained. As the value of the fitness function reduces, PSNR progress towards the

PSNR optimal and also the mean of NCj approximates to 1.0. The mathematical expression given for PSNR while assessing the image quality as well as NCC is specified below in Eqs. (14) and (15).

PSNR



F, F

 

⎛

= 10 log 10 ⎝

⎞

2552

x x k=1

and NC



 wm , wm



l=1

(F−F  )2

⎠

x×x

m m  wm (k, l) wm (k, l) k=1 = m l=1 m 2 k=1

l=1 (wm

(14)

(k, l))

(15)

Where F(k, l) and F (k, l) are the pixel values of coordinate (k, l) in the test medical image and the watermarked medical image, subscripts k and l signifies the position of the pixels in the corresponding images. x is the size of the test image which is square and  is the extracted watermark and m is the size of watermark. wm 5. Simulation results and analysis The proposed work is performed on MATLAB R2012a, and implemented on Windows 7 personal computer with 4 GB RAM and core 2 duo processor for testing the efficacy of proposed algorithm in terms of imperceptibility, robustness and capacity. The size of ROI and EPR watermark utilized for testing Leg fracture image are 170 × 200 and 145 × 250 pixels as depicted in Fig. 3. The test color image of leg fracture is shown in Fig. 3(a). The ROI portion as watermark which is used for tamper detection and diagnosis purpose is specified by the polygon in MRI test image of Fig. 3(b). and EPR watermark which includes electronic record and symptoms of the patient along with doctor ID is shown in Fig. 3(c). The size of medical test image is 1024 × 1024 pixels. The size of ROI watermark varies with the test image as the ROI portion diagnosed by the physician varies. It is well known fact that the watermarked image quality is decreased in accordance with the increase in size or the capacity of the watermark. The embedding capacity of the proposed algorithm is improved by using reversible LZW compression applied to EPR and ROI watermark prior to the insertion. LZW coding technique offers more reduction in number of bits and is preferred compared with other compression techniques. The EPR watermark is also compressed in the similar way as the ROI watermark. Then both the multiple watermarks were concealed within the RONI portion of the test image along with authentication code (hash). The algorithm results in highly transparent watermarking with multiple watermarking. The authentication code at transmitting and receiving side helps in recognition and recovery of the tamper in addition to the authentication in e-health applications. 5.1. Authentication, tamper recognition and lossless recovery In this algorithm, for authentication purpose, a hash value is generated using ROI portion of F. For generating hash, we employed

S. K, M. K and P. Kora / Biomedical Signal Processing and Control 55 (2020) 101665

Fig. 4. Secret key prior to watermarking.

tively by using Eq. (14) for gray scale images. For the color images Eq. (16) is used for calculating the PSNR PSNRp = 10 log10 x

x × x × max [F (k, l, p)]

x

k=1

Fig. 5. Extracted secret key after decompression of watermark.

Table 1 PSNR of watermarked and retrieved images of CT scan, ultrasound image, MRI image, X-ray image, spine, lung cancer, leg fracture, brain MRI. Test image

Watermarked image (dB)

Received images

CT scan Ultrasound image MRI image X-ray image Spine Lung cancer Leg fracture Brain MRI

33.18 34.89 36.99 35.33 34.87 33.24 34.55 32.68

Similar to original test image Similar to original test image Similar to original test image Similar to original test image Similar to original test image Similar to original test image Similar to original test image Similar to original test image

[34] hash algorithm based on SVD. The hash of test image at the transmitted end is compared with the hash of the test image at the receiving end. If they are identical, there is no tampering. If they are different, image is being tampered and requires replacement of original ROI in the tampered portion. The hash is in hexadecimal format comprising 128 bits and its value at transmitting and receiving end are shown in Figs. 4 and 5. Peak signal to noise ratio (PSNR) is calculated to evaluate the qualities of tampered as well as retrieved images. Table 1 depicts the PSNR of watermarked and tampered images in addition to retrieved images for several test images and it is found that the PSNR is above 32 dB. If the PSNR value is below 32 dB, then the medical image send for diagnosis is perceptually distorted and cannot be utilized for further checkup. Fig. 6 depicts the tamper recognition and lossless retrieval of MRI medical image. From Fig. 6 and Table 1, it is perceived that our watermarking system achieves 100% precision through recognizing the tamper and retrieving the ROI portion which is used in this paper as watermark along with EPR watermark which is used for authentication purpose, without any loss.

5.2. Imperceptibility 200 color and gray scale medical images downloaded from internet sources, www.image processing place.com and other web sites are taken to validate our medical watermarking scheme. Because of lack of space, four gray scale and color images of size 1024 × 1024 are used. The performance of proposed method is evaluated objec-

7

l=1 [F

2

(k, l, p) − F  (k, l, p)]

2

(16)

Where PSNRp (p = 1, 2, 3) specifies the PSNR of R, G and B component correspondingly and F(k, l, p), F (k, l, p) present the value of pixel (k, l) in component p of the host image F and that of the watermarked image F , and x indicates the spatial resolution of the host image F respectively. As shown in Figs. 7 and 8, the visible distinction is not seen between test medical image and its corresponding watermarked images. The average PSNR with values above 32 dB specify that the proposed scheme is efficient in generating good quality watermarked images. 5.3. Robustness The robustness of the proposed work between concealed dual watermarks and extracted dual watermarks for several attacks are evaluated by computing Normalized Correlation (NC). The quality of the extracted watermark is evaluated through computing the NC value by solving Eq. (15) respectively and the resultant outcomes are presented in Table 2. Higher values for NC specify that the extracted watermark is analogous to the original watermark. Table 2 illustrates the assessment of PSNR and NC obtained in the proposed work by inserting ROI watermark and EPR watermark in distinct medical images taken for the testing purpose. The maximum PSNR achieved by exploiting PSBFO algorithm for Lung cancer image is 37.24 dB when dual watermark is concealed. However, NC value is 1. From Table 2 there is an improvement seen in NC value when PSBFO algorithm is exploited and the NC values lies from 0.95 to 1. Conversely, the variation of NC values was recorded in the assortment of 0.81 to 0.89 when PSBFO algorithm is not utilized. It is implicit from Table 2 that the attained values of PSNR and NC constraints by exploiting the PSBFO algorithm are the topmost when compared to the algorithm without PSBFO. Table 3 depicts the results of the proposed work after concealing the multiple watermarks into the MRI test image counter to diverse attacks with regard to PSNR and NC values. Dissimilar kinds of attacks were executed to evaluate the performance of the PSBFO algorithm. The maximum NC value of “1” has been achieved counter to JPEG Compression attack with quality factor (QF) = 30. The NC values ranges from 0.85 to 1. For Histogram equalization the uppermost PSNR is attained as 35.68 dB. Thus the Robustness is apparent in contrast to certain attacks such as Rotation and cropping. As almost all the values of the normalized correlation are closer to “1” and the quality of the retrieved watermarks is also analogous to the original watermark, thus the robustness is achieved. Even after undergoing

Fig. 6. Tamper recognition and lossless retrieval of MRI medical image.

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Fig. 7. Medical test images of (a) CT scan of human brain, (b) Ultrasound image of 12 weeks fetus, (c) MRI image, (d) X-ray image, (e) MRI image of spine, (f) Lung cancer X ray, (g) Leg fracture MRI, (h) Brain MRI image.

Fig. 8. Watermarked medical test images of (a) CT scan of human brain, (b) Ultrasound image of 12 weeks fetus, (c) MRI image, (d) X-ray image, (e) MRI image of spine, (f) Lung cancer X ray, (g) Leg fracture MRI, (h) Brain MRI image.

Table 2 PSNR and NC values for different test images after inserting both EPR and ROI watermark. Test images

CT scan Ultrasound MRI image X-ray image Spine Lung cancer Leg fracture Brain MRI

PSNR (dB)

NCC

With PSBFO

Without PSBFO

With PSBFO

Without PSBFO

35.18 34.89 36.99 35.33 36.87 37.24 34.55 35.68

30.52 31.97 32.23 31.99 32.88 31.67 32.22 31.66

0.97 1 1 0.96 1 1 1 0.98

0.83 0.89 0.86 0.82 0.89 0.84 0.85 0.86

Table 3 Performance of PSNR and NC values for Attacks applied on MRI image with dual watermarks ROI and EPR. Types of attacks

PSNR (dB)

NCC

Resizing Rotation Cropping JPEG Compression (QF = 30) JPEG Compression (QF = 80) Salt and pepper noise (d = 0.01) Salt and pepper noise (d = 0.08) Gaussian (m = 0, v = 0.01) Gaussian (m = 0, v = 0.08) Histogram equalization

34.34 33.78 35.29 34.33 35.23 33.87 34.55 33.25 34.64 35.68

0.96 0.91 0.89 1 0.99 0.98 0.96 1 0.89 0.94

5.4. Payload dissimilar kinds of attacks as shown in Fig. 9 the dual watermark helps to authenticate the electronic record of the patient’s symptoms and disease along with the detection of tamper with 98% retrieval accuracy. Figs. 10 and 11 depicts extracted ROI and EPR watermark respectively from MRI image after applying multiple thresholds using PSBFO algorithm.

Payload is the number of bits essential to conceal the watermark into the test image. So, as the size of watermark increases there is a chance of perceptual degradation in the watermarked image, thus to keep safe the perceptual degradation in the watermarked image and at the same time to increase the watermark capacity, the watermark compression is essential which reduces the number

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Fig. 9. Attacks applied on watermarked medical image: (a) Resizing, (b) Rotation, (c) Cropping, (d) JPEG compression (QF = 30), (e) JPEG compression (QF = 80), (f) Salt and pepper noise (d = 0.01), (g) Salt and pepper noise (d = 0.08), (h) Gaussian Noise (m = 0 and v = 0.01), (i) Gaussian Noise (m = 0 and v = 0.08), (j) Histogram equalization.

Fig. 10. Extracted ROI watermark from attacks: (a) Resizing, (b) Rotation, (c) Cropping, (d) JPEG compression, (QF = 30), (e) JPEG compression (QF = 80), (f) Salt and pepper noise (d = 0.01), (g) Salt and pepper noise (d = 0.08), (h) Gaussian Noise (m = 0 and v = 0.01), (i) Gaussian Noise (m = 0 and v = 0.08), (j) Histogram equalization.

of bits required to conceal the watermark payload. As the watermark to be concealed in the medical image contains important data mainly related to diagnosis, so it must be compressed through loss-

less compression technique only, to avoid the loss of confidential data. Usually, the Payload acquired by the developed scheme is dependent on the dimension of medical image, dimension of the

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Fig. 11. Extracted EPR watermark from attacks: (a) Resizing, (b) Rotation, (c) Cropping, (d) JPEG compression (QF = 30), (e) JPEG compression (QF = 80), (f) Salt and pepper noise (d = 0.01), (g) Salt and pepper noise (d = 0.08), (h) Gaussian Noise (m = 0 and v = 0.01), (i) Gaussian Noise (m = 0 and v = 0.08), (j) Histogram equalization. Table 4 The dimensions of the RONI/ROI portion of various medical images with size 1024 × 1024. Test images

CT scan Ultrasound MRI image X-ray image Spine Lung cancer Leg fracture Brain MRI

Total count of bits

sion on dual watermarks before concealing the watermarks into the chosen RONI blocks. 5.5. Comparative analysis and discussions

Total count of RONI (bits)

Total count of ROI (bits)

1,083,904 1,082,224 1,081,920 1,062,304 3,112,828 3,113,228 3,111,728 3,108,228

14,400 16,080 16,384 36,000 32,900 32,500 34,000 37,500

ROI in addition to the total RONI parts considered for concealing the watermark in consort with the dimension of segregated blocks in the image, since the watermark is concealed within the RONI portion of the test image. The capacity of bits in the sectors of RONI rely on the count of selected blocks within the region which are opted through computing visual entropy in addition to edge entropy of all blocks and the smallest block values are considered for concealing the watermark. Table 4 provides the dimensions of the portions: RONI and ROI for the medical test images (CT scan, Ultrasound image, MRI image, X-ray image, Spine, Lung cancer, Leg fracture and Brain MRI) of 1024 × 1024 image size with 4 × 4 block size in conjunction with 2-level DWT. The size of the block is chosen as 4 × 4 with the intention of improving the watermarking capacity, but the disadvantage is that the time complexity of watermarking increases accessible payload capacities and varies with different watermark sizes for the different test images. Thus Table 5 provides the topmost bitcapacity of the RONI portion for dual watermarks in addition to the embedding position in DWT band. From Table 5, it is noteworthy to illustrate that the available capacity within the portion of RONI depends on the number of blocks selected through HVS characteristics and the portion of ROI of the test image. As the number of selected blocks and ROI portion varies with the test images the available capacity also varies accordingly as depicted in Table 5. Embedding location for dual watermarks in DWT band is also specified in Table 5. The capacity of the compressed watermark bits is very less than the numbers of bits are reduced after applying LZW compression and when compared with the available capacity of the proposed work depicted in Table 5 still there is a chance of concealing more number of watermark bits in the test image. Thus, the payload capacity of the proposed scheme is improved a lot through exploiting LZW lossless compres-

In Table 6 the robustness offered by the proposed scheme is verified and compared with robustness offered by the former related schemes as reported in [19,23,37] against attacks. It is identified that the proposed scheme achieves greater robustness compared to other schemes. In Parah et al. [19] the embedding factor is selected manually which is difficult task and also the results obtained with regard to constraints of the watermarking algorithm are not optimal. PSO is utilized in [23] to calculate the scaling factor while concealing the watermark into the transformed coefficients of discrete wavelets and depicts improved results contrasted with non-optimized scheme utilized in the same work. In [37] Genetic Algorithm is the first optimization algorithm exploited to identify the correlation between the pixel values of test image to attain enhanced estimation of neighboring pixel values, resulting in best stability among capacity of the payload and quality of the test image. Particle Swarm Optimization is the second optimization algorithm which is also implemented for the same purpose. Simulation outcome depicts that both PSO as well as GA almost provide the similar results, but GA surpasses the PSO. In the proposed work, the threshold “T” is not selected manually, instead the optimization algorithm (PSBFO) is exploited to choose the threshold value while concealing the watermark to attain optimum results with regard to imperceptibility and robustness constraints of any watermarking scheme. Thus Tables 6 and 7 illustrates the improvement in assessment values of PSNR and NC disparately for maximum attacks while testing between the recent works [19,23,37] and the proposed scheme. 5.5.1. Imperceptibility In the first scheme of [19] the average PSNR obtained is about 32.84 dB and it is 34.21 dB with regard to the second scheme. The author in [23] also presented two schemes to augment the consistency and robustness. The reliability of the first algorithm is established by concealing the principal components of the watermark into the test image by exploiting the transformed discrete cosine coefficients in which the average PSNR attained is nearer to 32 dB whereas in the second algorithm; those are concealed into the test image in transformed coefficients of discrete wavelets, then PSO is exploited in assessing the appropriate scaling factors to offer improved correlation coefficient as well as PSNR under numerous

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Table 5 Available payload capacities with different watermark sizes for the test images. Test images CT scan Ultrasound MRI image X-ray image Spine Lung Cancer Leg fracture Brain MRI

Size of watermark bits

Compressed watermark bits

Embedding location in DWT band

Available capacity in bits

ROI 120 × 120 EPR 150 × 250 ROI 120 × 134 EPR 170 × 250 ROI 128 × 128 EPR 160 × 240 ROI 180 × 200 EPR 164 × 250 ROI 140 × 235 EPR 165 × 200 ROI 130 × 250 EPR 150 × 220 ROI 170 × 200 EPR 145 × 250 ROI 150 × 250 EPR 175 × 200

7100 17,437 8000 21,678 8459 18,987 18,142 20,654 17,200 16,900 16,700 17,000 17,800 19,100 19,225 18,120

RONI(LH band) RONI(LH band) RONI(LH band) RONI(LH band) RONI(LH band) RONI(LH band) RONI(LH band) RONI(LH band) RONI(LH band) RONI(LH band) RONI(LH band) RONI(LH band) RONI(LH band) RONI(LH band) RONI(LH band) RONI(LH band)

340,457 464,231 387,382 495,000 398,654 479,943 424,598 490,212 860,452 780,230 699,724 724,659 925,621 1,012,342 984,754 925,184

Table 6 Assessment of PSNR value between the proposed work and the related schemes [19,23,37] for the ROI watermark and EPR watermark using MRI image under several attacks. Attacks

Resizing Rotation Cropping JPEG (QF = 30) JPEG (QF = 80) Salt and Pepper (d = 0.01) Salt and Pepper (d = 0.08) Gaussian Noise (m = 0, v = 0.01) Gaussian Noise (m = 0, v = 0.08) Histogram equalization

PSNR (dB) Parah et al. [19]

Run et al. [23]

Naseed et al. [37]

Proposed scheme

32.72 32.92 34.21 32.12 33.67 32.58 33.96 33.76 32.72 32.72

30.54 31.63 32.94 31.49 31.82 31.10 32.62 32.67 33.46 33.46

30.78 31.85 31.93 32.04 32.31 31.65 31.83 31.98 32.99 32.99

34.34 33.78 35.29 34.33 35.23 33.87 34.55 33.25 35.68 35.68

Table 7 Assessment of NC value between the proposed work and the related schemes [19,23,37] for the ROI watermark and EPR watermark using MRI image under several attacks. Attacks

Resizing Rotation Cropping JPEG (QF = 30) JPEG (QF = 80) Salt and Pepper (d = 0.01) Salt and Pepper (d = 0.08) Gaussian Noise (m = 0, v = 0.01) Gaussian Noise (m = 0, v = 0.08) Histogram equalization

NC Parah et al. [19]

Run et al. [23]

Naseed et al. [37]

Proposed scheme

0.8 0.79 0.7 0.84 0.74 0.84 0.83 0.87 0.79 0.79

0.62 0.67 0.75 0.76 0.63 0.74 0.78 0.75 0.72 0.72

0.65 0.64 0.72 0.7 0.7 0.71 0.69 0.7 0.67 0.67

0.96 0.91 0.89 1 0.99 0.98 0.96 1 0.89 0.89

attacks along with augment in robustness than the first algorithm. In the second method of [23] the average PSNR obtained is around 35 dB. The average PSNR obtained is around 31.9 dB in the first algorithm of [37] and 32.8 dB with regard to the second algorithm. So, it is proven that the proposed technique has improved imperceptibility and moreover the payload is also boosted a lot because of compressing the multiple watermarks with Lempel-Ziv-Welch (LZW) than these reported methods which is depicted in Table 9.

5.5.2. Robustness and payload In Table 7 NC Values of extracted ROI watermark and EPR watermark of the proposed scheme are evaluated for MRI test image with [19,23,37] under dissimilar kinds of attacks. From Table 7 it is noticed that the NC values of proposed scheme for all the attacks offer superior results compared to the related schemes. In [19,23,37] there is decline in the NC values when the noise density is increased, further the quality of extracted watermark is also degraded considerably with fall in JPEG quality factor. Conse-

quently these methods [19,23,37] cannot be exploited effectively when the compression ratio as well as noise densities are high. The proposed scheme improved the resultant performance in requisites of robustness as it is amalgamating Discrete Wavelet Transform in addition to Schur transform accompanied by PSBFO algorithm rather than relating them separately or the combination of DWTSchur or DCT-Schur. From Table 7 it is determined that the proposed scheme is more robust than [19,23,37]. Also, the outcome of the proposed algorithm is assessed and demonstrated in Table 8 for several Checkmark attacks [19] through Checkmark benchmarking software [35]. The proposed scheme is strong towards several image processing attacks listed in Checkmark toolbox. The attained perceptible quality of the watermarked medical image is accurate and greater than 31.5 dB for the listed checkmark attacks in Table 8, also the obtained NC values are more than 0.87 which are depicted in Table 7. Consequently, Table 8 confirms that the proposed method is presenting superior results in opposition to different Checkmark attacks as well. Payload of the proposed work

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Table 8 Assessment of PSNR and NC value between the proposed work and the related schemes [19,23,37] for the ROI watermark and EPR watermark using MRI image under checkmark attacks. Checkmark attacks

Removal of rows and columns Median filtering Motion blur Linear Shearing Weiner Warp Collage

Proposed method

Parah et al. [19]

Run et al. [23]

Naseed et al. [37]

PSNR (dB)

NC

PSNR (dB)

NC

PSNR (dB)

NC

PSNR (dB)

NC

32.49 33.67 31.98 34.74 32.67 34.12 32.43 33.45

0.89 0.88 0.93 0.91 0.92 0.9 0.94 0.95

35.73 34.12 33.10 33.84 35.06 37.25 35.71 34.98

0.76 0.79 0.74 0.72 0.77 0.8 0.73 0.72

36.17 32.02 34.23 35.79 36.66 35.89 33.66 31.83

0.71 0.72 0.79 0.75 0.71 0.78 0.77 0.76

34.96 40.78 35.81 36.76 34.69 39.17 35.73 36.19

0.74 0.77 0.81 0.79 0.76 0.73 0.7 0.69

Table 9 Payload capacity assessment of the proposed scheme with the relevant methods [19,23,37] on eight test images. Test images

CT scan Ultrasound MRI image X-ray image Spine Lung Cancer Leg Fracture Brain MRI

Payload capacity (bits) Parah et al. [19]

Run et al. [23]

Naseed et al. [37]

Proposed scheme

302,456 378,648 298,875 437,216 586,237 508,148 723,679 759,762

203,562 234,731 225,326 329,537 537,152 417,965 697,483 664,518

258,341 299,983 269,142 345,729 581,293 497,382 734,567 712,463

804,688 882,382 878,597 914,810 1,640,682 1,424,383 1,937,963 1,909,935

is improved a lot when related with [19,23,37] for the eight different medical test images as shown in Table 9. This is because the dual watermark is first compressed with LZW algorithm which is lossless, prior to concealing into the test images. As both watermarks are compressed, the number of redundant bits is reduced and a greater number of bits can be concealed without any loss as the algorithm is lossless which will lead to the improvement in payload capacity as specified in Table 9. Thus the main focus of the proposed scheme is achieved through drastic improvement in the Payload capacity compared to the few related schemes [19,23,37]. 5.5.3. Subjective assessment In general, the perceptual quality of medical images must be finally judged by doctors. So subjective assessment in addition to objective assessment is carried in this work. Thus a subjective assessment test is carried on medical image by showing original and watermarked gray/color medical images side by side to 100 doctors who unanimously approved the perceptual invisibility of concealed watermark. The experimental arrangement was performed on 200 medical images which comprise two copies for each medical image. One copy includes original medical image and another copy includes watermarked medical image. While conducting this test, the original as well as the watermarked copies were exhibited on the screen of computer and the doctors were requested to cautiously look at the exhibited medical images for several times, and further their opinion has to be given if any visual discrepancy has been found among the two medical images. Keeping this point in view Double Stimulus Continuous Quality Scale (DSCQS) assessment is used for assessing subjective fidelity criteria. The evaluation comprises of a continuous scale of five grades from bad to excellent. 5.5.4. Statistical analysis Statistical analysis based on Receiver Operating Curves for different attacks on medical watermarked test images are performed to further validate the proposed medical watermarking algorithm. In watermarking, false alarm probability is the probability of identifying the watermark, when the medical image is not watermarked and false rejection probability is the inability to recover extracted

watermark when medical image is watermarked. In watermarking generally a threshold for watermark detection is calculated by considering the size of watermark and false alarm probability which is specified in [36].

 cos1 (Td ) Pfd =

0

2



 2

0

sinm−2 (x)dx

sinm−2 (x)dx

(17)

where m is the size of watermark and Td is the detection threshold. The threshold Td , required to make Pfd = 10−6 is computed based on watermark having spatial resolution of 180 × 200. The threshold obtained is 0.026. To make comparison stochastically, ROC curves are drawn for attacks such as Resizing by a factor of 0.5, Rotation by 45◦ , cropping 0.25%, JPEG compression by QF = 30 and QF = 80, Salt & Pepper noise with d = 0.01, Salt & Pepper noise with d = 0.08, Gaussian noise with m = 0 and variance v = 0.01, Gaussian noise with m = 0 and variance v = 0.08 and Histogram equalization which is shown in Fig. 12(a)–(j). In Computer simulation of the proposed medical watermarking scheme, for watermark detection of all the attacks, the false alarm detection probability is taken as Pfa = 1 × 10−6 . The proposed method is compared with related algorithms [19] and [23]. In Fig. 12(a) it is found from the ROC curve of rescaling attack that the algorithm in [19] has a detection probability of 0.91 and the algorithm in [23] has 0.9 whereas our proposed technique has 1. It is found that all the three methods have good detection against resizing attack. In Fig. 12(b) when the watermarked medical image is rotated by 45◦ , a detection probability of 0.39, 0.28 and 0.97 is obtained respectively for [19,23] and proposed algorithm. In accordance with these outcomes, our proposed method performs a superior detection for this attack and surpasses over the related algorithms. In Fig. 12(c) when the watermarked medical image is cropped by 0.25%, a detection probability of 0.25, 0.2 and 0.97 is obtained respectively for [19,23] and proposed algorithm. Based on the outcome of this experimentation, our proposed method performs a superior detection for cropping attack and the results are better than the related algorithms in [19,23].

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Fig. 12. ROC curves for attacks: (a) Resizing, (b) Rotation, (c) cropping, (d) JPEG compression (QF = 30), (e) JPEG compression (QF = 80), (f) Salt and pepper noise (d = 0.01), (g) Salt and pepper noise (d = 0.08), (h) Gaussian Noise (m = 0 and v = 0.01), (i) Gaussian Noise (m = 0 and v = 0.08), (j) Histogram equalization.

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Table 10 Detection probability for attacks: (a) Resizing, (b) Rotation & cropping, (c) JPEG compression (QF = 30) & JPEG compression (QF = 80), (d) Salt and pepper noise(d = 0.01) & (d = 0.08), (e) Gaussian Noise (m = 0 and v = 0.01) & (m = 0 and v = 0.08), (f) Histogram equalization when Pfa = 1 ×10−6 . Attacks

Proposed method Parah et al. [19] Run et al. [23]

Resizing Rotation Cropping JPEG (QF = 30) JPEG (QF = 80) Salt and Pepper (d = 0.01) Salt and Pepper (d = 0.08) Gaussian noise (v = 0.01) Gaussian noise (v = 0.08) Histogram equalization

0.96 0.97 0.97 0.32 0.98 0.95 0.9 0.96 0.91 0.98

0.91 0.39 0.25 0.24 0.5 0.59 0.45 0.42 0.59 0.81

0.9 0.28 0.2 0.74 0.3 0.29 0.4 0.49 0.51 0.74

JPEG compression attack with a detection probability of 0.32 and 0.24 with QF = 30 for [19] and [23] respectively is identified, whereas our proposed technique has a detection probability of 0.9 for QF = 30 which is shown in Fig. 12(d). JPEG compression attack with a detection probability of 0.5 and 0.3 with QF = 80 for [19] and [23] respectively is identified, whereas our proposed technique has a detection probability of 0.98 for QF = 80 which is shown in Fig. 12(e). Salt and Pepper noise when applied on watermarked medical image, a detection probability of 0.59 and 0.29 with d = 0.01 for [19] and [23] respectively is identified, whereas our proposed technique has a detection probability of 0.95 for d = 0.01 which is shown in Fig. 12(f). Salt and Pepper noise when applied on watermarked medical image, a detection probability of 0.45 and 0.4 with d = 0.08 for [19] and [23] respectively is identified, whereas our proposed technique has a detection probability of 0.9 for d = 0.08 which is shown in Fig. 12(g). Gaussian noise when applied on watermarked medical image, a detection probability of 0.42 and 0.49 with d = 0.01 for [19] and [23] respectively is identified whereas our proposed technique has a detection probability of 0.96 for d = 0.01 which is shown in Fig. 12(h). Gaussian noise when applied on watermarked medical image, a detection probability of 0.59 and 0.51 with d = 0.08 for [19] and [23] respectively is identified whereas our proposed technique has a detection probability of 0.91 for d = 0.08 which is shown in Fig. 12(i). In Fig. 12(j) it is noticed that the algorithm in [19] has a detection probability of 0.81 and the algorithm in [23] has 0.74 whereas our proposed technique has a detection probability of 0.98 for histogram equalization attack. It is found that our proposed method performs a superior detection for this attack and surpasses over the related algorithms. The results obtained by the ROC curves for all the attacks confirms that out of the three methods the proposed method has average detection probability of 0.98 when compared with [19] and [23], thus the proposed algorithm reveals a good performance against detection of all attacks and outperforms the related methods. At last, the capability of detector is depicted for all attacks in Table 10 for the proposed and related methods when Pfa = 1 ×10−6 . 6. Conclusion In this research a novel blind, robust, secure and optimized medical image watermarking scheme is presented for concealing dual watermarks EPR and ROI using hybrid transforms of DWT and schur with PSBFO algorithm. These robust dual watermarks are mainly needed for tamper detection and authentication in Telemedicine. ROI watermark along with secret key is utilized for tamper detection as well as ROI authentication with lossless retrieval whereas

the EPR is utilized for the identification of the symptoms and record of the patient in the medical image to be tested. If the content of watermark is more, the perceptual degradation of the test image might occur which may create security problems. To overcome this and accomplish imperceptible medical watermarking, the compression of watermark is recommended. Keeping this point in view, multiple watermarks are compressed prior to concealing into the test image. By applying the LZW lossless compression algorithm on the multiple watermarks, the payload capacity of the developed technique is improved preserving the quality of the test image. The PSBFO algorithm is exploited to obtain the optimum threshold value “T” while concealing the dual watermarks into the test image through the integration of different transforms to attain optimum results keeping in consideration the constraints of the watermarking system. The authentication and tamper detection is achieved by concealing the ROI portion as watermark in the RONI portion. The algorithm has shown superior performance compared to other state of art algorithms in literature in terms of imperceptibility, robustness to check mark and signal processing attacks, capacity and security. In future, DWT is replaced with Quarterion DWT to withstand geometrical attacks. The remaining optimization algorithms hybrid combinations are explored to reduce computational time, number of generations focussing on constrained optimization. Further, the deep learning framework can be added to adaptively simulate attacks in blind manner. Conflicts of interest There are no conflicts of interest with this work. References [1] F.Y. Shih, Digital Watermarking and Steganography. Fundamentals and Techniques, CRC Press, 2017. [2] H. Nyeem, W. Boles, C. Boyd, A review of medical image watermarking requirements for teleradiology, J. Digit. Imaging 26 (2) (2013) 326–343. [3] S.C. Liew, J.M. Zain, Tamper localization and lossless recovery watermarking scheme, in: International Conference on Software Engineering and Computer Systems, Springer, Berlin, Heidelberg, 2011, pp. 555–566. [4] X. Guo, T.G. Zhuang, Lossless watermarking for verifying the integrity of medical images with tamper localization, J. Digit. Imaging 22 (6) (2009) 620. [5] S. Das, M.K. Kundu, Effective management of medical information through ROI-lossless fragile image watermarking technique, Comput. Methods Programs Biomed. 111 (3) (2013) 662–675. [6] K.A. Navas, S.A. Thampy, M. Sasikumar, EPR hiding in medical images for telemedicine, Int. J. Biomed. Sci. 3 (1) (2008) 44–47. [7] O.M. Al-Qershi, B.E. Khoo, Authentication and data hiding using a hybrid ROI-based watermarking scheme for DICOM images, J. Digit. Imaging 24 (1) (2011) 114–125. [8] K. Swaraja, Medical image region based watermarking for secured telemedicine, Multimed. Tools Appl. 77 (2018) 28249–28280. [9] K. Swaraja, 2017 protection of medical image watermarking, J. Adv. Res. Dyn. Control Syst. (JARDCS) (Special Issue 11) (2017) 480–486, ISSN: 1943–023X. [10] H. Nyeem, W. Boles, C. Boyd, Utilizing least significant bit-planes of RONI pixels for medical image watermarking, in: International Conference on Digital Image Computing: Techniques and Applications (DICTA), IEEE, 2013, pp. 1–8, November. [11] A.R.N. Nilchi, A. Taheri, A new robust digital image watermarking technique based on the discrete cosine transform and neural network, in: International Symposium on Biometrics and Security Technologies, IEEE, April, 2008, pp. 1–7. [12] V. Aslantas, An optimal robust digital image watermarking based on SVD using differential evolution algorithm, Optics Commun. 282 (5) (2009) 769–777. [13] V. Aslantas, A.L. Dogan, S. Ozturk, DWT-SVD based image watermarking using particle swarm optimizer, in: IEEE International Conference on Multimedia and Expo, IEEE, June, 2008, pp. 241–244. [14] V. Aslantas, S. Ozer, S. Ozturk, Improving the performance of DCT-based fragile watermarking using intelligent optimization algorithms, Optics Commun. 282 (14) (2009) 2806–2817. [15] Z. Wei, H. Li, J. Dai, S. Wang, Image watermarking based on genetic algorithm, in: 2006 IEEE International Conference on Multimedia and Expo, IEEE, July, 2006, pp. 1117–1120. [16] C.T. Yen, Y.J. Huang, Frequency domain digital watermark recognition using image code sequences with a back-propagation neural network, Multimed. Tools Appl. 75 (16) (2016) 9745–9755.

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