key: cord-1001246-lv0rj6v7
authors: Singh, Kamred Udham; Kumar, Akshay; Singh, Teekam; Ram, Mangey
title: Image-based decision making for reliable and proper diagnosing in NIFTI format using watermarking
date: 2022-04-29
journal: Multimed Tools Appl
DOI: 10.1007/s11042-022-12192-9
sha: d24e4eadaf3c1d551e0e9c1639cfd4ceb62a90c2
doc_id: 1001246
cord_uid: lv0rj6v7

Nowadays, advancement in Magnetic Resonance Imaging (MRI) and Computed Tomography Scan (CT-Scan) technologies have defined modern neuroimaging and drastically change the diagnosing of disease in the world healthcare system. These imaging technologies generate NIFTI (Neuroimaging Informatics Technology Initiative) images. Due to COVID-19 last several months CT-Scan has been performed on millions of the CORONA patients, so billions of the NIFTI images have been produced and communicate over the internet for the diagnosing purpose to detect the coronavirus. The communication of these medical images over the internet yielding the major problem of integrity, copyright protection, and other ethical issues for the world health care system. Another critical problem is that; is doctor diagnose the impeccable medical image of the patient because a large amount of COVID-19 patient’s data exists. For proper diagnosing it is also necessary to identify impeccable medical image. Therefore, to address these problems a secure and robust watermarking scheme is needed for these images. Various watermarking schemes have been developed for bmp, .jpg, .png, DICOM, and other image formats but the noticeable contribution is not reported for the NIFTI images. In this paper a robust and hybrid watermarking scheme for NIFTI images based on Lifting Wavelet Transform (LWT), MSVD (Multiresolution Singular Value Decomposition) and QR factorization. The combination of LWT, QR, and MSVD helps in retaining the sensitivity of the NIFTI image and improve the robustness of the watermarking scheme. In this scheme, multiple watermarks are inserted across the first slice of the NIFTI image. The proposed watermarking scheme is sustained against various noise attacks and performance is measured in terms of PSNR, SNR, SSIM, Quality of image, and Normalized correlation. Quality of the image is much significant that lie between .99994 to .99998 and SSIM reported from .94 to .99. Whereas the PSNR of the proposed scheme lies between 56.76 to 57.28 db and NC values lie between .9993 to .9998. which shows that the results are better than the existing schemes where PSNR is lies between 32.66 to 52.02 db. Watermarking, NIFTI, MSVD, LWT, QR and Image.

In recent years the fast and significant advances in information technologies (IT) have grown conceptual and application-level of medical imaging. Information technologies are the backbone of fast and accurate results in medical healthcare because the medical healthcare system such as Hospital Information System (HIS) used these technologies to get fast transmission of digital medical images over the internet for the diagnosing purpose and distribution of medical data all over the world. Telediagnosis, teleconsultation, and telesurgery (e-learning of the medical staff) are the application of telemedicine where swapping of medical information is required. Medical imaging technologies such as CT-scan, MRI, Color Doppler have phenomenal contributions to the modern healthcare system. These medical modalities produce the medical image in various formats viz. DICOM, NRRD, MINC, ANALYZE, NIFT, etc. and communicate to radiologist over the internet for the diagnosing and doctors get the diagnosing result very fast and accurate in compared to film-based imaging technologies. These filmless technologies have a major advantage over filmbased imaging is that many radiologists can access the same image at the same time at different places in the world and diagnose it. Filmless imaging technologies overcome the high printing cost and communication cost of these films, so film-based diagnosing shifted to filmless diagnosing. It is highly beneficial for the patient when he/she for from the doctor [34, 35] . Last some months COVID-19 spread all over the globe and affecting the social life of billions of peoples which arises the problem in front of the researchers to find the solution using digital imaging technologies to diagnose and estimation of COVID-19. But there is the main concern about the integrity and authenticity of electronic information and sensitive of medical information against malicious users and also concern the delivery of appropriate medical data too.

In the current COVID-19 situation, hospitals have a huge amount of patient's data generated by medical imaging equipment (medical images of patients) along with patient diagnosing history, & A novel non-blind watermarking scheme for NIFTI (CT-Scan and MRI images) images based on 2-D LWT, MSVD, and QR factorization has proposed. The combination of LWT and MSVD has provided the better robustness than existing schemes.

& It is an efficient watermarking scheme for NIFTI images, multiple watermarks are embedded into the image and extracted successfully from the watermarked image even after several kinds of attacks. & The evaluation of the proposed scheme is performed on NIFTI (.nii format) and the significant results achieved by the proposed scheme i.e. Peak Signal-to-Noise Ratio (PSNR) from 56 db to 57.27 db, Signal-to-Noise Ratio (SNR) from 45 db to 47 db, Structural Similarity Index Measure (SSIM) from .94 to .99, the Quality of Image index is 0.99 and the Normalized Correlation is .99. This paper is organized as follows: In Section 2 summary of some recently published study related to proposed work has been discussed in Section 2. LWT, QR, and MSVD are reviewed in Section 3 and the proposed watermarking scheme is introduced in Section 4. Experimental results and analysis of proposed work with some medical images are described in Section 5. Robustness analysis of the proposed scheme is discussed in Section 6. Finally, the conclusions are given in Section 7.

This section describes the methodology used in this research. In this research Multiresolution Singular Value Decomposition (MSVD), QR Factorization and, Lifting Wavelet Transform (LWT) have been used to develop a robust and secure hybrid watermarking scheme for NIFTI images.

Daubechies & Sweldens [7] proposed Lifting Wavelet Transform, in this transform construction is done in the spatial domain this is the main feature of the lifting scheme. It overcomes the limitations of the conventional wavelet transformation by decreasing the complexity of time and space (because it does not require complex mathematical calculations, unlike traditional wavelet transform). So computing wavelet transforms an efficient and most straightforward algorithm. Lifting Scheme does not rely on transforms from Fourier. Digital signals are basically a sequence of integers, but after the wavelet transform, the resultant message is a sequence of floating-point numbers as well; this hinders the possibility of a perfect or a reasonable reconstruction of the signal. A transformation algorithm should have a feasible reversible implementation. It preserves the domain of the message even after the transform i.e., it behaves as an operator, which transforms integer-based sequences again into integer-based sequence signals. Lifting scheme provides this facility that the domain of transform can be preserved (i.e., integer to integer) with better computational efficiency than the conventional wavelet transformations, making it a commonly used change in image processing [9, 19, 28] . Construction of a signal by using a lifting scheme consists of the following three steps:

& Split -In the split phase, the given signal ω(n) is split into non-overlapping odd ω o (n) and

even ω e (n) sets as: ω e (n) = ω(2n), ω o (n) = ω(2n + 1) & Predict -In this step, odd set is predicted from even set. The polynomial components occurring are compensated in the high pass by the predict phase. So this phase is also known as high pass filtering operation phase. Now odd sets ω o (n) are predicted using even set ω e (n) and the difference φ(n) is produced as

Where P r [.] is the predict operator and φ(n) is high-frequency component. φ(n) is the error between original set and the predicted value.

& Update -The even set ω e (n) are updated using wavelet coefficient to calculate scaling function.

In this stage, moments in low pass is also preserved. Therefore, this phase is also known as low pass filtering operation phase. By applying an update operator, U p [.] the low-frequency component L(n) represents a coarse approximation of the original signal ω(n) defined as

LWT decomposes the host image into four sub-bands viz. LLLH, HL, and HH. LL sub-band has the low-frequency components that contain most of the energy of the image and other subbands (LH, HL, and HH) have high-frequency components that contain less energy of the image. The LWT can be applied several times on the LL band to decomposing into sub-bands because the LL band contains most of the energy of the image. Figure 1 shows the 2-level LWT. Here in Fig. 1 (b) the blue square portion shows the LL sub band.

In QR decomposition [1] , a non-singular matrix of size m × n .

Where Q is an orthogonal matrix with size m × m and R is an upper triangular matrix. The row of Q matrix is generated by the applying Gram-Schmidt. A and Q is denoted as A = [a 1 , a 2 , ……. ., a n ] and Q = [q 1 , q 2 , ……. ., q n ] The upper triangular matrix R can be computed as:

. . . . . . ; a n ½ ¼q 1 ; q 2 ; . . . . . . ; q n ½ r 11 r 12 r 1n 0 r 22 r 2n 0 0 r nn

. . . 0 0 0 a n ; q n h i For example, A is an image block size of 3 × 3 is represented as follows: 

MSVD basically used in real-time applications, its computational process is very simple. It doesn't have a fixed set based on vectors like Discrete Cosine Transform (DCT), Fast Fourier Transform (FFT) and Wavelet. One resolution feature is simple to distinguish, but the characteristics of multiresolution approaches are imperceptible. It is the substitution of the wavelets and is comparable to the Wavelet Multi-Resolution Analysis (MRA) since it breaks down the host picture into distinctive frequency bands (lower frequency and higher frequency LL, LH, HL and, HH) [23] . MSVD has low computational than the wavelet transformation, and directional information such as in wavelet transformations is not given. In MSVD, decomposition is based on the data set [27] . It can be utilized at many levels and deliver robustness against the various geometrical image processing attack viz. cropping, and rotation. For a matrix, A with dimension M * N, the singular value decomposition (SVD) of A into the three-matrix as shown in Eq. (2)

Here V and U are orthonormal, and matrix S is a diagonal matrix in descending order of singular values. MSVD for image (I) of size M × N is computed by initially reshaping I to a matrix I′ of size 4 × MN/4. Therefore, the singular value decomposition of I′ is shown in Eq. (3) as:

Now, matrix T is shown by Eq. (4) as:

where matrix U of size 4X4, matrix S of size 4 × MN/4 and matrix T of size [4 × MN/4]. In matrix T rows I, II, III, and IV are reshaped to produce size matrices M/2 × M/2. Such reformatted matrices are the image sub-bands i.e., LH, HL, LL, HH. The LL band includes the lower frequency information, and the high-frequency information is found in other sub-bands i.e., HL, LH, and HH. In this paper, we use LH band for watermarking. Figure 2 depict the structure of MSVD.

Slices of NIFTI image and watermark both are grayscale scale. At first, metadata is put away securely from the NIFTI image, and then select the first slice for watermarking to insert the watermark. Initially apply the MSVD on this slice which breaking down it into four distinctive frequency sub-bands i.e. LL, LH, HL, and HH. Now select the HL sub-bands because it contains the most positive values. Furthermore, we apply the LWT on the HL band which decompose it into four different sub-band frequency, viz. LL, LH, HL, and HH. The LL frequency sub-band contains the vital data of the image and is resilient to the de-noising attack. Paralleling, the primary components of the watermark are subjected to QR factorization and the formation of a new vector. The augmentation of the watermark in the host slice is controlled via the scaling factor (alpha). Furthermore, inverse transformed (ILWT) is performed to reconstruct the slice. Finally, augment the metadata with this watermarked slice and generate the watermarked NIFTI image. The proposed algorithm is illustrated below and is also explained pictorially in Fig. 3 . Input: CT-Scan NIFTI image Size (630 X 630)

Grayscale Watermark Size (32X32) in .bmp format Output: Watermarked NIFTI image

Step 1 Extract metadata from the host NIFTI image and select the first slice for watermarking.

Step 2 Perform MSVD on the selected slice that decompose it in four sub-bands LL, LH, HL and HH. Step 3 Perform two-level LWT on the HL band of MSVD which decompose the image into four sub-bands LH, HL, LL, and HH Step 4 Apply the QR Factorization on the watermark and generate the new vector V.

Step 5 Vector V is embedded into transformed components of the host image by selecting the LL band. Watermark is embedded in the image as below

x ′ is the transformed component of the watermark image. y is the factorized watermark component.

x is the transform image of host image. α is the embedding strength.

Step 6 Perform Inverse Lifting Wavelet Transformation of the final watermarked image.

Step 7 Perform inverse Multiresolution Singular Value Decomposition on the final watermarked image.

Step 8 Embedded the metadata in the watermarked image and generate the watermarked NIFTI image.

The watermark extraction is a crucial task of watermarking scheme; Fig. 4 depicts the watermark extraction process of the proposed watermarking scheme. It shows that the original and watermarked NIFTI image is needed to separate the watermark from the watermarked NIFTI image. At first, metadata is isolated from both original and watermarked image and then select the first image slice. Furthermore, we apply the MSVD and LWT on the original and watermarked to get the embedded noisy component and afterward perform the inverse QR factorization on these noisy segments to produce the watermarks.

Input: M x N NIFTI image I; and M x N NIFTI watermarked image I +.

Output: Watermark W

Step 1 Extract metadata from both the NIFTI images i.e. I and I + .

Step 2 Select the first slice of both images I and I + .

Step 3 Perform MSVD on both images I and I + to obtain LL, LH, HL and, HH sub-bands.

Step 4 Perform LWT on HL band of both images I and I + to obtain LL, LH, HL and, HH sub-bands.

Step 5 Extract the watermark from the watermarked image using the original image by performing the reverse watermark embedding steps.

where I ′ is the watermarked image. y is the factorized watermark component. I is the original image. α is the embedding strength. Step 6 Perform inverse QR factorization on extracted components to get the extracted watermark.

In this section, the results of the proposed watermarking scheme are tested over NIFTI images generated by the CT-Scan taken from public medical databases https://zenodo.org/record/ 3757476#.X0pwCnkzZPZ [20] under ethical medical practices, without revealing the patient's identity and only for the experimental purpose. The proposed watermarking scheme has been executed on the intel Core TM i7-5500U CPU 2.5 GHz Windows10 machine with 8 GB RAM on the MATLAB platform. Various NIFTI images with a resolution of 630 × 630 pixels and 8-bit grayscale watermark of BMP format with resolution 32 × 32 pixels are used for experiment.

The analysis of the proposed watermarking technique is verified over the ten different NIFTI images. The outcomes are investigated with respect to image quality estimations viz. PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error), NC (Normalized Correlation), SSIM (Structural Similarity Index Measure) and Q. PSNR and NC, SSIM are utilized as a measure for perceptual imperceptibility. For the watermark detectable quality assessment, PSNR is used, which is delineated as follows [16, 26, 33] :

To measure the robustness of the watermarking scheme NC are utilized between the original image I and the watermarked image I + , if NC is near to one then the robustness of the scheme is significant. NC is evaluated using the following equation [32] .

Universal image quality index Q [36] is defined as follows.

., N} and y = {y i | i = 1, 2, 3…. ., N.}x and y are the original and the test image signals respectively. Universal image quality index Q is the product of three components can be defined as:

Structural Similarity Index Measure (SSIM), is utilized to assess the likeness between the actual image and watermark image. SSIM value lies between −1 to +1, if SSIM = 1, at that point, original image and watermark image are exactly similar.

Where μ x : the average of actual image and μ y : the average of watermarked image. σ 2 x: the variance of original cover and σ 2 y : the variance of watermarked image with covariance σ xy . Where, c 1 and c 2 are free parameters.

The analysis and testing of the proposed scheme has been done on ten different NIFTI images, taken from public database [20] , which are Img1, Img2, Img3, Img4, Img5 and Img6 to determine the imperceptibility and robustness. The test results of the proposed scheme are depicted in Figs. 5 and 6. The metadata of the original NIFTI image is also preserved inside the watermarked NIFTI image. Table 1 exhibits the performance proportions of watermarking scheme over the NIFTI images from Img1 to Img6. Figure 7 (a) exhibits the graphical understanding of NC between the first NIFTI image slice and watermarked image slice. Figure 7 (b) & (c) shows the PSNR and SNR between the original and watermarked image respectively. Figure 7 (d) speaks to the SSIM of the proposed which is more like one. Figure 8 depicts the graphical interpretation of quality measurements Q of the scheme.

Analysis of the proposed scheme has been done based on NC, SSIM and Q are having values roughly near one; that indicating excellent robustness and imperceptibility between the original and watermarked images. PSNR of the proposed scheme is more than 57 dB, which recommends that the watermarked image quality is extremely considerable. Note: After successful extraction of the watermark from the received NIFTI image medical practitioners or radiologists assure that they diagnose the impeccable medical image. It is very

Extracted Watermark

Img2 Img3 Img4 Img5 Img6 Img7 Img8 Img9 Img10 Fig. 6 Extracted watermarks from Ten medical images Another smart role of the watermark is that it can be a logo or an image that contains some unique patient's information such as patient id, patient name, etc. as per convenience. Therefore, this scheme may play a vital role in the authentication of the NIFTI images to produce correct diagnosing which are procreate in huge amounts due to COVID-19. 

The results of the proposed scheme are compared with the other existing scheme in Table 3 that the results are more The proposed watermarking scheme embedded the different quantities of watermarks over the image slice. The most extreme watermark inclusions in the NIFTI image without hampering the diagnosis of watermarked NIFTI image exhibited in Table 2 . These images contain sensitive patient's clinical data as well. In this way, it is fundamental that the nature of these images must not be debased subsequent to watermarking. This scheme is tested and analyzed with a different number of watermark addition and found that the outcomes are promising. Table 2 exhibits that the image quality measurements are legitimately relative to the quantity of watermarks addition and no noticeable change has been noticed. Figure 9 , represent the PSNR, of the proposed scheme with the different number of watermark insertion. The PSNR evaluation has been done at ten different NIFTI images. In Fig. 9 we can see that the estimation of PSNR is increased when the decreasing the number of inserted watermarks. In the event that we assess the PSNR and SNR of the img1 at 24 watermark insertions & 2 watermark insertions, there are serious variation in PSNR is 45.95 dB to 57.27 dB. Thus, if the number of wateremark insertion is incresead then the PSNR value will be decrease.

There is no huge difference in NC which is evaluated on ten different images. The NC results of the proposed scheme with the different number of watermark insertions are depicted in Fig. 10 . The graph shows that the higher values of NC will be obtained when minimum insertion has been done in the original image. The high correlation at the two watermark insertion is 0.9999 i.e. image quality is good. But when the 24 watermarks inserted in the NIFTI image the NC value is 0.9978 which is also considerable for the scheme. Figure 11 depicted the image quality index (Q) of the proposed watermarking scheme at the different number of watermarks insertion and reported that there is no massive difference in Q. The image quality has been evaluated on various NIFTI images, graph shows that if the number of watermarks insertion is increased the value of Q has decreased and vice versa. The value of Q is high at the minimum insertion i.e., 0.9999 and at the 24 watermarks insertion value is 0.9986 which is also good. Thus, the proposed watermarking scheme gives good results even when the number of watermark insertion is increased.

SNR is another important performance measure of the watermarking scheme. In above Figure (Fig. 12) we can see that the SNR of the proposed watermarking scheme at the different number of watermarks insertion. The SNR has been assessed on different NIFTI images, graph shows that if the number of watermarks insertion is increased the value of SNR has decreased and vice versa. The value of SNR is high at the two watermark insertion i.e., 47.10 and at the 24 watermarks insertion value is 35.95 which is likewise acceptable. Consequently, the proposed watermarking scheme gives great outcomes in any event when the quantity of watermark insertion is increased.

The results of the proposed scheme are compared with the other existing scheme (Zairi et al. [37] , Takore et al. [31] , BW & Permana [34] , Thanki et al. [33] , Hsu et al. [15] , Goli & Naghsh [13] , Ernawan et al. [8] , Awasthi et al. [3] ) in Table 3 . The PSNR of proposed scheme is 56.76 to 57.28 and NC is .9993 to .9998 whereas the PSNR and NC values of other existing scheme is lies between 32.66 to 52.02. and 0.9647 to 1 respectively. Therefore, the outcomes of the proposed watermarking scheme are more significant.

The above Figure (Fig. 13 ) depicted the comparison of the PSNR of the proposed scheme with the other existing scheme (Zairi et al. [37] , Takore et al. [31] , BW & Permana [34] , [3] is 48.92 to 52.02. Therefore, the PSNR of the proposed watermarking scheme is more significant than the other schemes. Figure 14 shows the comparison of the NC of the proposed scheme with the other existing scheme (Zairi et al. [37] , and Thanki et al. [33] ). The PSNR of the proposed scheme lies between 56.76 to 57.28 whereas the PSNR of other existing schemes is lies between .9993 to .9998 (NC of Zairi et al. [37] is 1, and the NC of Thanki et al. [33] is 0.9647to 0.9827. Takore et al. [31] , BW & Permana [34] , Thanki et al. [33] , Hsu et al. [15] , Goli & Naghsh [13] , Ernawan et al. [8] , Awasthi et al. [3] ) we observed that the outcomes are more significant.

Robustness test of proposed watermarking scheme has done, by performing the various image processing attacks viz. Salt & Pepper, Gaussian noise, Poisson, Speckle on watermarked image which may incite the clearing of watermarks. After applying the attack, if the extraction of the inserted watermark is successfully done from the altered watermarked image then the scheme is robust and legitimate in nature. A major requirement of any significant watermarking scheme is the sustenance of the scheme against these attacks. Insertion of watermarks and quality of watermarked image is inversely proportional to each other but as per the necessity, a trade-off must be considered. Subsequently, this scheme is tested with various watermarks insertion which prompts increments in robustness and degradation in image quality, but the minimum number of watermarks insertion increases the quality of the image. Various noise attacks have been applied over the watermarked image to determine the robustness of the scheme. Proposed scheme designed for CT-Scan medical image, so the diagnosis will be framed on this watermarked NIFTI image. Along these lines, any critical degradation in the watermarked image quality isn't worthy since it might cause the life and demise of the patient. Thus, the scheme is tested with the various noise attacks with the various boundaries, till the quality measurements indorse that the altered watermarked image is of high calibre.

JPEG compression with quality factor 90 and 75 applied over the watermarked slice and embedded watermark extracted successfully. Table 4 depict this attack for the NIFTI images and the findings define that the efficiency of the is promising.

Salt and pepper noise with noise density 0.001 to 0.004 applied on the watermarked slice of NIFTI image, and the watermark is successfully extracted. Table 5 depict that this attack for the slices of the NIFTI images and the findings define that the efficiency of the scheme is promising.

Speckle noise attack applied with variance 0.001 to 0.003 on the watermarked slice of the NIFTI image and the watermark is successfully extracted by the proposed watermarking scheme. Table 6 depict the evaluation of the attack over the slice of the NIFTI image and the findings show that the result of scheme is significant.

Poisson noise attack applied over the watermarked slice of the NIFTI image and then watermark is extracted successfully by the proposed scheme. In Table 7 , analysis of the attack for the NIFTI images are tabulated and the results show that the performance of proposed scheme is favourable.

Gaussian noise attack applied with variance 0.001 and varying it from 0.001 to 0.004 on the watermarked slice of the NIFTI image. After the attack extraction of watermark is successful.

In Table 8 , analysis of the attack for the slice of NIFTI images are tabulated and the results show that the performance of proposed scheme for NIFTI images is promising.

In this paper, a novel non-blind watermarking scheme based on LWT, QR, and MSVD for NIFTI images is proposed. These images have delicate clinical image data, so the watermarking of these images is a very crucial task. For the experimental purpose we have used NIFTI images (630 × 630) sourced from CT-Scan and grayscale watermark (32 × 32). The obtained outcomes of the measuring parameter of the watermarked image (NC, Q, SNR, PSNR, and SSIM) are acceptable are par tradeoff among the imperceptibility, robustness, and number of watermark insertion. The quality of the image is much significant that lie between [3] ) we observed that the outcomes are more significant.The proposed scheme is robust against the various noise attacks and JPEG compression. Further, the comparison of the outcomes of the proposed scheme with the existing scheme showed that the performance proposed scheme better than the existing schemes. In this scheme first slice of the NIFTI image (NIFTI images have various numbers of slices) is used for the watermarking to authenticate the NIFTI image. If someone wants to insert watermark in more than one or all slice of the image, then this scheme can be applied on each slice of NIFTI image which he/she want to use for the authentication purpose. Thus, the proposed watermarking scheme can be used for the authentication and identification of the appropriate medical image for the diagnosing of CT-Scan and MRI image in NIFTI images format.

Parallel detection algorithm using multiple QR decompositions with permuted channel matrix for SDM/OFDM

Medical image watermarking for ownership & tamper detection

Image watermarking using APDCBT in selected pixel blocks

A fuzzy based ROI selection for encryption and watermarking in medical image using DWT and SVD. Multimedia tools and applications

Digital image authentication and recovery: employing integer transform based information embedding and extraction

A new colour image copyright protection approach using evolution-based dual watermarking

Factoring wavelet transforms into lifting steps

An improved image watermarking by modifying selected DWT-DCT coefficients

Spiht algorithm based on fast lifting wavelet transform in image compression

A robust image watermarking method based on DWT, DCT, and SVD using a new technique for correction of main geometric attacks

Reversible watermarking algorithm based on wavelet lifting scheme

Optimized color image watermarking through watermark strength optimization using particle swarm optimization technique

Introducing a new method robust against crop attack in digital image watermarking using two-step sudoku

Robust and imperceptible watermarking scheme based on canny edge detection and SVD in the contourlet domain

Multimedia Tools and Applications

A blind robust QR code watermarking approach based on DCT

Crypto watermarking of images for secure transmission over cloud

A blind proposed 3D mesh watermarking technique for copyright protection

A robust image watermarking based on DWT-QR decomposition

Multi-Objective Genetic Algorithm Optimization for Image Watermarking Based on Singular Value Decomposition and Lifting Wavelet Transform

COVID-19 CT lung and infection segmentation dataset

Image Denoising using multiresolution singular value decomposition transform

LWT-QR decomposition based robust and efficient image watermarking scheme using Lagrangian SVR

A chaos-based image encryption algorithm based on multiresolution singular value decomposition and a symmetric attractor

November) Tampering estimation watermarking based on lifting wavelet and chaotic sequence

Tampering estimation watermarking based on lifting wavelet and chaotic sequence

Exploiting de-noising convolutional neural networks DnCNNs for an efficient watermarking scheme: a case for information retrieval

MSVD Based Image Watermarking in NSCT Domain

Efficient analysis of hybrid directional lifting technique for satellite image denoising

Color image blind watermarking scheme based on QR decomposition

Optimized image watermarking with artificial neural networks and histogram shape

March) A modified blind image watermarking scheme based on DWT, DCT and SVD domain using GA to optimize robustness

Multi-level security of medical images based on encryption and watermarking for telemedicine applications

An efficient medical image watermarking scheme based on FDCuT-DCT

Medical image watermarking with tamper detection and recovery using reversible watermarking with LSB modification and run length encoding (RLE) compression

Dual watermarking in tele-radiology using DWT for data authentication and security

A universal image quality index

March) An algorithm for digital image watermarking using 2-level DWT, DCT and QR decomposition based on optimal blocks selection

Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations