A secure framework for medical image by integrating watermarking and encryption through fuzzy based ROI selection

Abstract

Security, secrecy, and authenticity problems have arisen as a result of the widespread sharing of medical images in social media. Copyright protection for online photo sharing is becoming a must. In this research, a cutting-edge method for embedding encrypted watermarks into medical images is proposed. The proposed method makes use of fuzzy-based ROI selection and wavelet-transformation to accomplish this. In the first step of the process, a fuzzy search is performed on the original picture to locate relevant places using the center region of interest (RoI) and the radial line along the final intensity. The suggested method takes a digital picture and divides it into 4×4 non-overlapping blocks, with the intent of selecting low information chunks for embedding in order to maximize invisibility. By changing the coefficients, a single watermark bit may be inserted into both the left and right singular SVD matrices. The absence of false positives means the suggested technique can successfully integrate a large amount of data. Watermarks are encrypted using a pseudorandom key before being embedded. Discrete wavelet transform saliency map, block mean method, and cosine functions are used to construct an adaptively-generated pseudo-random key from the cover picture. Images uploaded to social media platforms must have a high degree of invisibility and durability. These watermarking features, however, come with a price. The optimal scaling factor is used to strike a balance between the two in the proposed system. Furthermore, the suggested scheme’s higher performance is confirmed by comparison with the latest state-of-the-art systems.

Keywords

Watermarking key component wavelet transform Fuzzy ROI encryption

1 Introduction

The many social media platforms provide a wonderful opportunity to interact and engage with people on a global scale. It is used extensively in a variety of contexts, including the dissemination of information, social interaction, the collection of news, and decision-making [1]. In social media, the most common form of communication is via the use of digital photos. Users of social media platforms upload and share billions of photographs every single day [2]. When digital photographs are shared on social media platforms, users run the risk of exposing themselves to a number of security risks, including unauthorized access, altering or copying of the image, morphing, and tampering with the image’s content [3]. The use of digital picture watermarking is one of the strategies that shows the greatest promise for resolving these problems [4]. In order to successfully transmit watermarked photos via an internet platform, you need to ensure that you have satisfied all three of the most important criteria for watermarking: imperceptibility, resilience, and embedding capability. The embedding technique that was used to cover up the watermark inside the cover picture is the one that determines how these criteria should be fulfilled [5, 6]. Spatial and spectral strategies are the two primary approaches that may be used to hide the watermark in the cover picture. The great embedding capacity and imperceptibility of spatial approaches are both advantages, but the lack of resilience is a disadvantage [7, 8]. Spectral techniques, on the other hand, have a greater resilience, but they suffer from a poor imperceptibility. Researchers are looking at hybrid transformations in order to reach high levels of imperceptibility and resilience in their work [9, 10]. However, there is a balance to be struck between the three watermarking criteria. By selecting an optimum scaling factor (embedding strength) for the purpose of watermark embedding, one may guarantee that a balance between the aforementioned trade-offs is maintained. Researchers have used approaches called Nature Inspired Metaheuristic Optimization, or NIMO, to maximize scaling factor and, as a result, balance the trade-off between different watermarking needs [11]. In order to successfully hide the watermark inside the cover picture, the embedding strength is determined by the optimal scaling factor. However, developing effective fitness functions for optimization in order to strike a balance between the capacities of exploration and exploitation remains a difficult task [12].

2 Related work

Spatial, spectral, and hybrid watermarking methods for digital images have all been studied in the previous several decades [13, 14]. The basic goals of any watermarking technique for digital images are to prevent the host image’s quality from degrading too much as a result of the watermark and to show that it can withstand a variety of assaults. Discrete cosine transform (DCT) - Singular value decomposition (SVD) hybrid transforms approaches are used for watermark embedding in the digital picture watermarking systems reported in [15, 16]. In [15], a watermarking system is proposed that does not care about watermark security. Since the embedding scaling factor is selected at random, it may not work well with certain picture modalities. In [16], a blind watermarking system based on an Arnold map is presented as a means of providing security. But it’s defenses against geometric and noise-based assaults are weaker. Similarly, Discrete wavelet Transform-Singular value decomposition hybrid transformations are used for watermark embedding in the techniques suggested in [17 –19]. Even if the watermarking, detection, and decryption stages of the technique suggested in [18] are all commutative, the scheme’s overall resilience is inadequate. Using a hyper chaotic map in four dimensions, the authors of [19] encrypt watermarks. Improved fruit fly optimization is proposed for maximizing scaling factors. As a result, this system is more vulnerable to assault. In addition, Integer wavelet Transform-Singular value decomposition hybrid transformations are used for watermark embedding in the digital picture watermarking systems presented in [20, 21]. The false positive error issue of the SVD during picture transmission is addressed by the watermarking approach based on several scaling factors proposed in [22 –28]. Watermarking is done using a map that is purposefully messy. In [29], we offer a reliable multi-objective ant colony optimization watermarking approach. In order to insert watermarks, it employs the fundamental component method. When compared to other possible attack vectors, this approach has a lower risk of being exploited. Telemedicine relies on the safe transmission of medical pictures, which is why the watermarking system presented in [30 –36] was developed. For encryption purposes, the watermarking system presented in [37, 38] additionally makes use of a secret security key that is adaptively generated from cover and watermark pictures.

The most important contributions made by the proposed strategy are as follows:

When the region of interest (ROI) is found using a fuzzy based model, the compactness of partitions is improved by identifying to include the spatial function with the membership function, and to locate the crucial locations along the radial line.

Superior watermark protection at a lower than average computational cost. A method known as Random Pixel Position Swapping is used in order to encrypt the watermark such that it conforms to the pseudo-random key. In order to improve watermark security and make it more difficult to detect, encrypted watermarks are inserted into low-information blocks.

In embedding, SVD coefficient modification increases imperceptibility and resistance against geometrical, filtering, and noising assaults.

3 Proposed method

After performing fuzzy ROI identification in the source medical picture, our proposed method continues by performing a second-level wavelet decomposition and SVD on the original image. On the picture containing the watermark, there will be two rounds of discrete wavelet transform (DWT) carried out, with substitution occurring at the first level and permutation occurring at the second level. After a singular value decomposition has been carried out to identify the primary constituent, the singular values of both the original medical image and the watermarked version are then modified accordingly. The image with the watermark is generated by using the inverse wavelet transform. The process of watermarking is shown in graphical form in Fig. 1.

Fig. 1

Watermarking process.

3.1 Selecting ROI through Fuzzy

If you use ROI areas to include the watermark, will results in leading to an incorrect diagnosis. However, RONI watermarking systems have various problems, including the fact that they can only be implemented if RONI exists, the quantity of information to be encoded depends on the RONI region size, and ROI may not be secured against malicious assaults. In Fig. 2 we see a diagrammatic depiction of the ROI extraction process.

Fig. 2

ROI extraction.

In recent years, one of the unsupervised strategies for clustering that goes by the name of Fuzzy C-Means (FCM) clustering [11] has seen a significant rise in its level of use. These promising methods make use of spatial locality [36] in order to cluster data, and they find extensive use in areas such as medical imaging, image segmentation, and remote sensing. The SS FCM method and this technology are both included into our approach in order to produce a new hybrid system. This brand-new approach makes use of the membership value function of the pixels that are next to one another. We may observe a general version of the spatial function represented by Equation 1.

$S_{i_{k}}^{u} = \sum_{j \in NB (p_{k})} U_{ij}$ (1)

U_ij is the initial membership function, and NB (p_k) is the corresponding 5×5 window that is centred on pixel (p_k).

Equation 2 is a representation of the membership function after the spatial function has been added to it. $U_{i_{k}}^{u} = \frac{U_{i_{k}}^{u} S_{i_{k}}^{u}}{\sum_{j = 1}^{c} U_{j_{k}}^{u} S_{j_{k}}^{u}}$ (2)

It is possible to calculate the membership function $U_{i_{k}}^{u}$ by first splitting the host picture pixel into c clusters. Once this is done, the membership function may be included into the spatial function $S_{i_{k}}^{u}$ . In order to aggregate the data points that will be used as labels, defuzzification is done on each individual pixel. After that, the method of maximum membership is used in order to create the segmented image. The only possible output value for our fuzzy system is either one (result area in), or nothing at all (outside ROI).

3.2 Optimization parameter

Tuning of both the algorithm-specific and the controlling parameters is essential for the success of evolutionary and swarm-based optimization methods. In order to optimise the scaling factor, we employ the fitness function shown in Equation 3

$\begin{matrix} F = & \frac{PSNR + UACI}{ω} \\ + (\sum_{N = 1}^{M} NC (N) + \sum_{N = 1}^{M} BER (N)) / ω \end{matrix}$ (3)

Where PSNR and UACI are metric to calculate imperceptibility whereas NC and BER are matric to evaluate robustness. ω represents the chosen population strength.

3.3 Watermarking algorithm

Input: F_W, Host image, Optimization Parameter (OP);

Output: Encrypted watermarked image (E_W_I);

Step 1: Decompose ‘Host image’ via IWT.

IWT (Host image) = [cLL, cHL, cLH, cHH]

Step 2: Apply Schur Decomposition on cLL sub-band.

Schur(cLL) = [ULL, SLL]

Step 3: Obtain the singular vector of ‘ULL ‘via RSVD.

RSVD(ULL) = [ULL, SLL, VLL]

Step 4: Modify (embed) the ‘SLL’

SLL + EF×W final = WSLL

Step 5: Attain marked Schur matrix by applying inverse RSVD.

ULL×WSLL×(VLL) T = WSLL

Step 6: Obtain the marked IWT sub-band via inverse Schur.

ULL×WSLL×(ULL) T = WLL

Step 7: Obtain the marked image (WF_max) via inverse IWT.

inverse IWT (WLL, HL, LH, HH) = Final img

Step 8: Obtain the encrypted marked image

4 Results and discussions

In order to evaluate the efficacy of the proposed system, simulation tests are carried out in MATLAB 2017b. Images of CT scans, MRI scans, and ultrasound scans (all 512 by 512 pixels in size) together with a single watermark picture (64 by 64 pixels in size) are utilised. Performance is measured in terms of their capacity, complexity, resilience, reversibility, and detectability. An unoptimized MATLAB code model is used to simulate the three ROI-based watermarking strategies. For the sake of a level playing field, we only take into account the processing time during the ROI extraction phase and not during embedding or extraction. Table 2 represents the NPCR and UACI comparison.

Table 2
Examining the UACI and NPCR outcomes through other encryption techniques

Image Type NPCR Proposed UACI Proposed

[14] [16] [19] Technique [14] [16] [19] Technique

a 0.9223 0.9338 0.9903 0.9186 0.3381 0.3514 0.2516 0.3723

b 0.9335 0.9452 0.9968 0.93 0.3373 0.357 0.2785 0.3737

c 0.9456 0.9469 0.9991 0.9317 0.3471 0.3554 0.2688 0.3683

d 0.9252 0.9338 0.972 0.9186 0.3387 0.3579 0.2883 0.376

e 0.9354 0.9538 0.9665 0.9386 0.3426 0.3566 0.2503 0.3687

f 0.9468 0.9723 0.9626 0.9571 0.345 0.3547 0.2981 0.3723

g 0.9554 0.9643 0.9608 0.9491 0.342 0.3554 0.2882 0.3685

h 0.9358 0.9722 0.9792 0.957 0.3437 0.358 0.2583 0.3721

i 0.9361 0.9534 0.9663 0.9382 0.3451 0.3548 0.263 0.3693

j 0.9525 0.9742 0.9559 0.959 0.3427 0.3614 0.2684 0.3687

k 0.9628 0.9725 0.9723 0.9573 0.3436 0.3568 0.261 0.3696

l 0.9354 0.9338 0.9364 0.9186 0.3428 0.3546 0.2723 0.3707

Image Type	NPCR			Proposed	UACI			Proposed
a	0.9223	0.9338	0.9903	0.9186	0.3381	0.3514	0.2516	0.3723
b	0.9335	0.9452	0.9968	0.93	0.3373	0.357	0.2785	0.3737
c	0.9456	0.9469	0.9991	0.9317	0.3471	0.3554	0.2688	0.3683
d	0.9252	0.9338	0.972	0.9186	0.3387	0.3579	0.2883	0.376
e	0.9354	0.9538	0.9665	0.9386	0.3426	0.3566	0.2503	0.3687
f	0.9468	0.9723	0.9626	0.9571	0.345	0.3547	0.2981	0.3723
g	0.9554	0.9643	0.9608	0.9491	0.342	0.3554	0.2882	0.3685
h	0.9358	0.9722	0.9792	0.957	0.3437	0.358	0.2583	0.3721
i	0.9361	0.9534	0.9663	0.9382	0.3451	0.3548	0.263	0.3693
j	0.9525	0.9742	0.9559	0.959	0.3427	0.3614	0.2684	0.3687
k	0.9628	0.9725	0.9723	0.9573	0.3436	0.3568	0.261	0.3696
l	0.9354	0.9338	0.9364	0.9186	0.3428	0.3546	0.2723	0.3707

4.1 Performance analysis for proposed watermarking

The watermarking intensity of a picture may be evaluated in two ways: the peak signal-to-noise ratio (PSNR) and the normalized cross correlation (NCC). Table 1 represents the test images used for simulation experiments. It is clear from Table 2 that the watermarking strength of the methods reported in [16] is much lower than that of the proposed approach. Table 3 provides a comparison of PSNR and SSIM across a variety of systems. Table 4 displays the factors considered while comparing different methods.

Table 1
Test Images

misc_4.1.01 (a) misc_4.1.02 (b) misc_4.1.03 (c) misc_4.1.04 (d)

misc_4.1.05 (e) misc_4.1.06 (f) misc_4.1.07 (g) misc_4.1.08 (h)

misc_4.1.09 (i) misc_4.1.10 (j) misc_4.1.11 (k) misc_4.1.12 (l) Watermark Image

Table 3

PSNR and SSIM Comparison

Images	[3]		[8]		Proposed
	PSNR	SSIM	PSNR	SSIM	PSNR	SSIM
misc_4.1.01	38.32	0.7918	39.87	0.9046	42.19	0.8536
misc_4.1.02	38.07	0.744	39.08	0.8333	41.38	0.8426
misc_4.1.03	38.98	0.8175	40.17	0.9064	42.33	0.9175
misc_4.1.04	38.24	0.7464	39.14	0.8275	41.17	0.7982
misc_4.1.05	40.41	0.7767	41.92	0.8648	43.75	0.8393
misc_4.1.06	38.67	0.7345	39.28	0.835	41.7	0.8415
misc_4.1.07	39.57	0.7699	40.01	0.8515	41.97	0.8057
misc_4.1.08	41.97	0.7722	42.23	0.8558	44.41	0.8094

Table 4

Comparing PSNR values

Source Image	Watermark	[3]	[5]	[8]	[10]	Proposed Method
						Encryption + watermarking	Fuzzy ROI + Encryption + Watermarking
		42.402	41.974	45.458	46.16	46.374	46.55
		43.652	38.512	41.498	45.674	46.152	46.488
		42.418	40.754	43.66	46.758	46.482	46.644
		40.401	38.954	42.55	45.852	46.348	46.44
		41.958	37.418	43.655	44.702	46.228	46.332

Calculation of Signal to Noise Ratio is shown in Equation 4 $PSNR = 10 \log_{10} (\frac{255^{2}}{(\frac{1}{m}) \sum_{i = 1}^{m} H_{i} - H_{i}^{'}})$ (4)

The correlation coefficient between the initial watermark and the resulting watermark is determined using NCC. This is shown by the equation Equation 5. Table 5 represents the NC comparison between proposed and existing systems.

Table 5

NC Comparison

Attack Type	Attack Degree		[21]	[26]	[32]	[35]	Proposed Method
							Without Fuzzy ROI	With Fuzzy ROI
Gaussian LPF	Window size	3 × 3	0.962	0.925	0.964	0.989	0.925	0.935
		4 × 4	0.832	0.827	0.827	0.928	0.911	0.937
Sharpening	Window size	3 × 3	0.952	0.985	0.974	0.910	0.984	0.927
AWGN	Variance	0.001	0.722	0.724	0.814	0.921	0.794	0.851
Salt &pepper	Density	0.1	0.881	0.793	0.914	0.901	0.891	0.924
Cropping		[20 20 400 480]	0.82	0.783	0.822	0.847	0.883	0.89
Rotation		10°	0.69	0.685	0.685	0.786	0.869	0.901
Scaling		2	0.81	0.843	0.832	0.768	0.942	0.962
Histogram Equalization			0.911	0.918	0.901	0.946	0.937	0.956

$NCC = \frac{\sum_{k = 1}^{w_{p} \times w_{q}} w_{k} w_{k}^{'}}{\sqrt{\sum_{k = 1}^{w_{p} \times w_{q}} w_{k}^{2} \sum_{k = 1}^{w_{p} \times w_{q}} w_{k}^{' 2}}}$ (5)

5 Conclusions

We present an approach that combines watermarking and encryption to protect diagnostically processed medical images. Given that the suggested technique achieves a PSNR of around 49.5, it secures watermark information without degrading picture quality. A high embedding rate with little distortion is shown by the proposed approach. As a result, you can be certain that the watermarked picture is of great quality and delivers a substantial payload. The suggested technique has been tested on grayscale photos, but it will eventually be able to handle colour images as well.

References

Kumar

, Aggarwal

, Rani

, et al., Secure video communication using firefly optimization and visual cryptography, Artif Intell Rev 55 (2022), 2997–3017. 10.1007/s10462-021-10070-8

Haghighi

B.B.

, Taherinia

A.H.

, Harati

and Rouhani

, WSMN: An optimized multipurpose blind watermarking in shearlet domain using MLP and NSGA-II, Appl Soft Comput 101 (2021), 107029.

, Wang

G.G.

and Gandomi

A.H.

, A survey of learning-based intelli- gent optimization algorithms, Arch Comput Methods Eng 28(5) (2021), 3781–3799.

Singh

K.N.

and Singh

A.K.

, Towards integrating image encryption with com-pression: A survey, ACM Trans Multimedia Comput, Commun, Appl (TOMM) 18(3) (2022), 1–21.

Fares

, Khaldi

, Redouane

and Salah

, DCT & DWT based watermarking scheme for medical information security, Biomed Signal Process Control 66 (2021), 1746–8094.

Begum

, Ferdush

and Uddin

M.S.

, A hybrid robust watermarking system based on discrete cosine transform, discrete wavelet transform, and singular value decomposition, J King Saud Univ-Comput Inf Sci (2021), 1319–1578.

Suganyadevi

, Renukadevi

, Balasamy

and Jeevitha

, Diabetic Retinopathy Detection Using Deep Learning Methods, 2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT), (2022), pp. 1–6. https://doi.org/10.1109/ICEEICT53079.2022.9768544

Maheshkar

, An optimized color image watermarking technique using differential evolution and svd–dwt domain, in: Proceedings of Fifth In- ternational Conference on Soft Computing for Problem Solving, Springer, Singapore, (2016), pp. 105–116.

Nazir

, Bajwa

I.S.

, Samiullah

, Anwar

and Moosa

, Robust secure color image watermarking using 4D hy- perchaotic system, DWT, HbD, and SVD based on improved FOA algorithm, Secur Commun Networks 2021(6617944) (2021).

10.

Zainol

, The

J.S.

and Alawida

, An FPP-resistant SVD-based image wa- termarking scheme based on chaotic control, Alex Eng J 61(7) (2022), 5713–5734.

11.

Balasamy

, Krishnaraj

and Vijayalakshmi

, Improving the security of medical image through neuro-fuzzy based ROI selection for reliable transmission, Multimed Tools Appl 81 (2022), 14321–14337. https://doi.org/10.1007/s11042-022-12367-4

12.

Maheshkar

, Region-based hybrid medical image watermarking for secure telemedicine applications, Multimedia Tools Appl 76(3) (2017), 3617–3647.

13.

Alshoura

W.H.

, Zainol

, The

J.S.

and Alawida

, A new chaotic image watermarking scheme based on SVD and IWT, IEEE Access 8 (2020), 43391–43406.

14.

Devi

K.R.

, Suganyadevi

, Karthik

and Ilayaraja

, Securing Medical Big data through Blockchain technology, 2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS), (2022), pp. 1602-1607, doi: 10.1109/ICACCS54159.2022.9785125.

15.

Sharma

, Sharma

and Sharma

J.B.

, An adaptive color image watermarking using RDWT-SVD and artificial bee colony based quality metric strength factor optimization, Appl Soft Comput 84 (2019), 105696.

16.

Zeebaree

D.Q.

, Robust watermarking scheme based LWT and SVD using artificial bee colony optimization, Indonesian J Electr Eng Comput Sci 21(2) (2021), 1218–1229.

17.

Balasamy

, Krishnaraj

and Vijayalakshmi

, Improving the security of medical image through neuro-fuzzy based ROI selection for reliable transmission, Multimed Tools Appl 81 (2022), 14321–14337. https://doi.org/10.1007/s11042-022-12367-4

18.

Najafi

and Loukhaoukha

, Hybrid secure and robust image wa- termarking scheme based on SVD and sharp frequency localized contourlet transform, J Inf Secur Appl 44 (2019), 144–156.

19.

Gao

, Mou

, Banerjee

, Cao

, Xiong

and Chen

, An effective multiple- image encryption algorithm based on 3D cube and hyperchaotic map, J King Saud Univ-Comput Inf Sci 34(4) (2022), 1535–1551.

20.

Balasamy

, Krishnaraj

and Vijayalakshmi

, An Adaptive Neuro-Fuzzy Based Region Selection and Authenticating Medical Image Through Watermarking for Secure Communication, Wireless Pers Commun (2021). https://doi.org/10.1007/s11277-021-09031-9

21.

Attaullah Shah

and Jamal

S.S.

, An improved chaotic cryptosystem for image encryption and digital watermarking, Wirel Pers Commun 110(3) (2020), 1429–1442.

22.

Salehnia

and Fathi

, Fault tolerance in LWT-SVD based image watermarking systems using three module redundancy technique, Expert Syst Appl 179 (2021), 0957–4174.

23.

Luo

, Gong

, Zhou

, et al., Adaptive and blind watermarking scheme based on optimal SVD blocks selection, Multimedia Tools Appl 79(1) (2020), 243–261.

24.

Suganyadevi

, Shanmuga Priya

, Kiruba

, Gomathi

and Kalshetty

J.N.

, Classification of EEG signals using Machine learning algorithms, 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), (2022), pp. 1–6, doi: 10.1109/MysuruCon55714.2022.9972364.

25.

Rani

, Kumar

and Goel

, Image Modelling: A Feature Detection Approach for Steganalysis. In: Singh, M., Gupta, P., Tyagi, V., Sharma, A., Ören, T., Grosky,W. (eds) Advances in Computing and Data Sciences. ICACDS 2016, Communications in Computer and Information Science 721 (2017). Springer, Singapore. https://doi.org/10.1007/978-981-10-5427-3_15.

26.

Ullah

, Jian

, Hussain

, Guo

, Yu

, Wang

and Yin

, A brief survey of visual saliency detection, Multimedia Tools Appl 79(45) (2020), 34605–34645.

27.

Suganyadevi

, Priya

S.S.

, Menaha

, Sathiya

, Jha

and S

S.B.

, Smart Healthcare in IoT using Convolutional Based Cyber Physical System, 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), (2022), pp. 1–6, doi: 10.1109/MysuruCon55714.2022.9972679.

28.

Ali

, Ahn

C.W.

and Pant

, A robust image watermarking technique using SVD and differential evolution in DCT domain, Optik 125(1) (2014), 428–434.

29.

Chang

C-C.

, Tsai

and Lin

C.-C.

, SVD-based digital image watermarking scheme, Pattern Recognit Lett 26(10) (2005), 1577–1586.

30.

Rao

, Jaya: A simple and new optimization algorithm for solving con- strained and unconstrained optimization problems, Int J Ind Eng Comput 7(1) (2016), 19–34.

31.

Xin-she

, Nature-Inspired Metaheuristic Algorithms, Luniver Press (2008), 83–96.

32.

Khan

W.A.

, Hamadneh

N.N.

, Tilahun

S.L.

and Ngnotchouye

J.M.

, A review and comparative study of firefly algorithm and its modified versions, Optim Algorithms-Methods Appl 45 (2016), 281–313.

33.

The USC-SIPI image database, 2021, Available online: https://sipi.usc.edu/ database/. (Accessed 09 October 2021).

34.

The Kaggle image database, 2021, Available online: https://www.kaggle.com/datasets. (Accessed 09 October 2021).

35.

Katoch

, Chauhan

S.S.

and Kumar

, A review on genetic algorithm: Past, present, and future, Multimedia Tools Appl 80(5) (2021), 8091–8126.

36.

Prabha

and Sam

I.S.

, Robust color image watermarking by elliptical phase modification based on Walsh Hadamard transform and triangular vertex transform, Sa¯dhana¯ 46(1) (2021), 1–16.

37.

Balaska

, Ahmida

, Belmeguenai

and Boumerdassi

, Image encryption using a combination of grain-128a algorithm and Zaslavsky chaotic map, IET Image Process 14(6) (2020), 1120–1131.

38.

Sehra

, Raut

, Mishra

, Kasturi

, Wadhera

, Saxena

G.J.

and Saxena

, Robust and secure digital image watermarking technique using Arnold transform and memristive chaotic oscillators, IEEE Access 9 (2021), 72465–72483.


misc_4.1.01 (a)	misc_4.1.02 (b)	misc_4.1.03 (c)	misc_4.1.04 (d)

misc_4.1.05 (e)	misc_4.1.06 (f)	misc_4.1.07 (g)	misc_4.1.08 (h)

misc_4.1.09 (i)	misc_4.1.10 (j)	misc_4.1.11 (k)	misc_4.1.12 (l)	Watermark Image

A secure framework for medical image by integrating watermarking and encryption through fuzzy based ROI selection

Abstract

Keywords

1 Introduction

2 Related work

3 Proposed method

4 Results and discussions

Table 1 Test Images misc_4.1.01 (a) misc_4.1.02 (b) misc_4.1.03 (c) misc_4.1.04 (d) misc_4.1.05 (e) misc_4.1.06 (f) misc_4.1.07 (g) misc_4.1.08 (h) misc_4.1.09 (i) misc_4.1.10 (j) misc_4.1.11 (k) misc_4.1.12 (l) Watermark Image

References

Table 1
Test Images

misc_4.1.01 (a) misc_4.1.02 (b) misc_4.1.03 (c) misc_4.1.04 (d)

misc_4.1.05 (e) misc_4.1.06 (f) misc_4.1.07 (g) misc_4.1.08 (h)

misc_4.1.09 (i) misc_4.1.10 (j) misc_4.1.11 (k) misc_4.1.12 (l) Watermark Image