Hessenberg Decomposition Research Articles

An Efficient Cryptographic Scheme based on Optimized Watermarking Scheme for Securing Internet of Things

In this work, a new efficient cryptographic scheme based on the concept of chaotic map and optimized watermarking scheme is proposed. In the optimized watermarking scheme, a combination of discrete wave transformation (DWT), hessenberg decomposition (HD), and singular value decomposition (SVD) are used. In this, the host image is first broken down into several sub-bands using multi-level DWT, and the resulting coefficients are then fed into HD during the embedding phase. Simultaneous watermark operation is performed on SVD. Finally, the scale factor embeds the watermark into the host image. The Differential evolution method is used to find the best scaling factor for the optimized watermarking scheme. The resulting watermarked image is then encrypted by the session key based scheme. In this scheme for each image encryption, a new random session key will be produced. The presented approach uses 64-bit plaintext and a variable size key that will be decided at the time of encryption for encrypting an image. Since session keys change with each transmission, this approach does not involve extracting and remembering session keys in order to produce subsequent session keys. IoT devices are used to test the developed method for security. The experiment’s findings shows that the suggested method works better than the current scheme in several aspects.

Enhancing medical image security through machine learning and dual watermarking-based technique

ABSTRACT As the world becomes increasingly digital, healthcare is no exception. With the ease of sharing e-healthcare records over open networks, smart healthcare systems have become a popular way to manage patient information. But as the popularity of these systems has grown, so has the concern for their security. That's where image security techniques come in. In this paper we have developed a new approach to secure e-patient records like DICOM images. By combining the redundant discrete wavelets transform (RDWT), Hessenberg Decomposition (HD), and randomized singular value decomposition (RSVD). We developed a robust and dual watermarking scheme. This scheme uses multiple watermarks, including Electronic Patient Record (EPR) as text and images, to ensure high-level authentication. In order to attain a balance between imperceptibility and robustness, a PSO-based optimization of scale factor is employed and Turbo code is utilized to encode the EPR and minimize channel noise. Additionally, the marked image undergoes encryption using a 3D chaotic-based encryption technique, and the extracted watermark is denoised through a deep neural network. The result shows that the proposed scheme is both secure and reliable. With this dual watermarking scheme, we have made great strides in securing e-patient records.

IWT based robust and secure color image watermarking using Hessenberg decomposition and SVD

IWT based robust and secure color image watermarking using Hessenberg decomposition and SVD

A watermarking scheme for color images that achieves optimality using the Transit Search Algorithm

This paper describes an innovative watermarking method that combines discrete wavelet transform (DWT), Hessenberg decomposition (HD), and singular value decomposition (SVD). To do this, the main image and the watermark are divided into three channels (red, green and blue – RGB). Then, each part of the main image individually undergoes the steps of DWT, HD and SVD, while the watermark components are processed by SVD. Insertion of the watermark is carried out by adjusting the singular values of the watermark and the main image, using a watermark scaling factor (α). The optimal choice of α poses a challenge, so the transit search algorithm is employed to find a trade-off between visibility and robustness. To evaluate this method, comparisons are made with other studies using various optimization algorithms such as particle swarm optimization, artificial bee colony and fly optimization algorithm. The results of the experiments confirm the effectiveness of this technique.

An effective embedding algorithm for blind image watermarking technique based on Hessenberg decomposition

An effective embedding algorithm for blind image watermarking technique based on Hessenberg decomposition

Dual Watermarking for Security of COVID-19 Patient Record

In recent years, smart healthcare systems have gained popularity due to the ease of sharing e-patient records over the open network. The issue of maintaining the security of these records has attracted many researchers. Thus, robust and dual watermarking based on redundant discrete wavelet transform (RDWT), Hessenberg Decomposition (HD), and randomized singular value decomposition (RSVD) are put forward for CT scan images of COVID-19 patients. To ensure a high level of authentication, multiple watermarks in form of Electronic Patient Record (EPR) text and medical image are embedded in the cover. The EPR is encoded via turbo code to reduce /eliminate the channel noise if any. Further, both imperceptibility and robustness are achieved by a fuzzy inference system, and the marked image is encrypted using a lightweight encryption technique. Moreover, the extracted watermark is denoised using the concept of deep neural network (DNN) to improve its robustness. Experiment results and performance analyses verify the proposed dual watermarking scheme.

Strong Robustness Watermarking Algorithm Based on Lifting Wavelet Transform and Hessenberg Decomposition

Watermark imperceptibility and robustness in the present watermarking algorithm based on discrete wavelet transform (DWT) could be weakened due to data truncation. To solve this problem, a strong robustness watermarking algorithm based on the lifting wavelet transform is proposed. First, the color channels of the original image are separated, and the selected channels are processed through lifting wavelet transform to obtain low-frequency information. The information is then split into blocks, with Hesseneberg decomposition performed on each block. Arnold algorithm is used to scramble the watermark image, and the scrambled watermark is transformed into a binary sequence that is then embedded into the maximum element of Hessenberg decomposed matrix by quantization modulation. The experimental results exhibit a good robustness of this new algorithm in defending against a wide variety of conventional attacks.

Robust Watermarking Method for Secure Transmission of Medical Images in EHR Systems

Confidentiality of Electronic Health Record (EHR) and privacy are two important security requirements for healthcare systems. Many devices on the EHR network utilize little or no encryption, which makes data in transit vulnerable to exploitative attacks, such as Man-in-the-Middle and other filtration methods. Recently, watermarking algorithms as an efficient response to these requirements is in the underline. In this paper, we present a robust watermarking method conceived as part of an Electronic Health Record platform. In this method a chaotic encryption and blind medical image watermarking technique was incorporated into the system as an authorization mechanism to ensure confidentiality and integrity of electronic health information. We present a hybrid watermarking method based on a combination of discrete wavelet transform (DWT), hessenberg Decomposition (HD), Singular value decomposition (SVD) and an original chaos crypto system based on the Arnold Transform (AT) of Singular Value Decomposition. In order to spread the robustness of our algorithm and provide additional security, an improved SVD-AT embedding and extraction procedure has been used to scramble the EHR data in the preprocessing step of the proposed method. In the process of watermark embedding, an R-level discrete wavelet transform was applied to the host image. The low frequency wavelet coefficients are selected to carry this scrambled-watermark. In extraction process, the stored used plain image is obtained from the trusted authority server to complete the verification process. The receiver should compare the unsigned watermark with the extracted watermark again. The verification can be done before clinical procedures and diagnosis. The proposed watermarking method endures entirety attacks and rightly extracts the hidden watermark without significant degradation in the image quality, thus, when the Peak Signal to Noise Ratio (PSNR) and Normalized Correlation (NC) performance of the proposed algorithm is performed.

A robust blind watermarking scheme based on lifting wavelet transform and hessenberg decomposition

In this paper, a novel robust blind grayscale image digital watermarking scheme is introduced based on lifting wavelet transform (LWT) in combination with discrete cosine transform (DCT), Hessenberg decomposition, and entropy analysis for copyright protection of multimedia information. At first, the two levels of LWT are applied to the host grayscale image to improve the imperceptibility of the watermarking scheme and then the high-frequency sub-band of the 2-level of LWT is decomposed by DCT. Next, the DCT coefficients are divided into 4×4 non-overlapping blocks. After that, Hessenberg decomposition performs on each selected block, whereas the first row, first column element of the upper Hessenberg matrix is utilized to hide the watermark. To evaluate the imperceptibility and robustness of the proposed digital watermarking scheme, the peak signal-to-noise ratio (PSNR), and normalized cross-correlation (NC) are utilized to measure the quality and the ability of the proposed watermarking scheme to robust against signal processing operations and geometric attacks. Experimental and analysis results have demonstrated that the proposed scheme is achieved a very good tradeoff between imperceptibility and robustness. The comparison with other scheme have shown that the proposed digital watermarking schemes have a superior performance in terms of imperceptibility and robustness than other.

Color Ultrasound Image Watermarking Scheme Using FRT and Hessenberg Decomposition for Telemedicine Applications

Watermarking is a valuable technique for verifying medical images obtained through the internet for diagnosis. There is a greater need for security in medical pictures with ever- increasing security risks. This research presented a Finite Ridgelet Transform (FRT)-Hessenberg based watermarking scheme in medical images. The suggested paradigm is divided into two stages. Before watermark insertion, FRT is applied to medical images. The coefficients are combined into blocks of 4 x 4 and each block is decomposed using Hessenberg decomposition. The second column of the Q matrix is used to insert the watermark using the additive quantization technique. The results obtained from our experiment have given good visual quality of the watermarked images. The high PSNR value 53.6121 and NC value 1.0 show that our scheme is performing better. Moreover, the performance of our scheme is robust against several attacks. The consequences of this result imply that the anticipated scheme is effective for medical image watermarking.

Robust Zero-Watermarking Algorithm for Medical Images Using Double-Tree Complex Wavelet Transform and Hessenberg Decomposition

With the rapid development of smart medical care, copyright security for medical images is becoming increasingly important. To improve medical images storage and transmission safety, this paper proposes a robust zero-watermarking algorithm for medical images by fusing Dual-Tree Complex Wavelet Transform (DTCWT), Hessenberg decomposition, and Multi-level Discrete Cosine Transform (MDCT). First, the low-frequency sub-band of the medical image is obtained through the DTCWT and MDCT. Then Hessenberg decomposition is used to construct the visual feature vector. Meanwhile, the encryption of the watermarking image by combining cryptographic algorithms, third-party concepts, and chaotic sequences enhances the algorithm’s security. In the proposed algorithm, zero-watermarking technology is utilized to assure the medical images’ completeness. Compared with the existing algorithms, the proposed algorithm has good robustness and invisibility and can efficiently extract the watermarking image and resist different attacks.

Authentication of NIfTI Neuroimages Using Lifting Wavelet Transform, Arnold Cat Map, Z-Transform, and Hessenberg Decomposition

The technological progress in digital medical imaging has enabled the diagnosis of various ailments, and thus upgraded the global healthcare system. In the era of coronavirus 2019 (COVID-19), telemedicine plays the crucial role of supporting remote medical consultation in rural locations. During the remote consultation, numerous medical images are sent to each radiologist via the Internet. There has been a surge in the number of attacks on digital medical images worldwide, which severely threatens authenticity and ownership. To mitigate the threat, this paper proposes a robust and secure watermarking approach for NIfTI images. Our approach painstakingly incorporates a watermark into the chosen NIfTI image slice, aiming to accurately fit the watermark, while preserving the medical information contained in the slice. Specifically, the original image was converted through the lifting wavelet transform (LWT), realizing excellent modification during insertion. Next, Z-transform was applied over the low-low (LL) band, and the Hessenberg decomposition (HD) was performed on the transformed band, which contains the maximum energy of the image. Afterwards, Arnold Cat map was employed to scramble the watermark, before inserting it into the slice. Simulation results show that our approach strikes a perfect balance between security, imperceptibility, and robustness against various attacks, as suggested by metrics like peak signal-to-noise ratio (PSNR), normalized correlation (NC), structural similarity index measure (SSIM), and universal image quality index Q.

Double-Matrix Decomposition Image Steganography Scheme Based on Wavelet Transform with Multi-Region Coverage.

On the basis of ensuring the quality and concealment of steganographic images, this paper proposes a double-matrix decomposition image steganography scheme with multi-region coverage, to solve the problem of poor extraction ability of steganographic images under attack or interference. First of all, the cover image is transformed by multi-wavelet transform, and the hidden region covering multiple wavelet sub-bands is selected in the wavelet domain of the cover image to embed the secret information. After determining the hidden region, the hidden region is processed by Arnold transform, Hessenberg decomposition, and singular-value decomposition. Finally, the secret information is embedded into the cover image by embedding intensity factor. In order to ensure robustness, the hidden region selected in the wavelet domain is used as the input of Hessenberg matrix decomposition, and the robustness of the algorithm is further enhanced by Hessenberg matrix decomposition and singular-value decomposition. Experimental results show that the proposed method has excellent performance in concealment and quality of extracted secret images, and secret information is extracted from steganographic images attacked by various image processing attacks, which proves that the proposed method has good anti-attack ability under different attacks.

ROI-Fuzzy Based Medical Data Authentication Scheme for Smart Healthcare System

During the current pandemic, telemedicine applications encourage radiologists to consult patients remotely. Medical images are typically sent over the internet for easy access when diagnosing a patient. Intruders can manipulate these images during communication. Therefore, we require a method of authentication for these images that is capable of authenticating these images before diagnosis. To address these issues, a watermarking scheme has been developed within the scope of this paper. The foundation of anticipated watermarking system is Regions of Interest (ROI), using FRT to RONI, and converted into blocks; afterward, Hessenberg decomposition is applied on blocks selected by means of fuzzy logic. The binary watermark is quantized into the medical image. It has made the watermarking system non-fragil and secure.The proposed scheme’s performance is evaluated on various attack situations, including noise and scale assaults. Watermarked pictures with enhanced PSNR ranging from 56.9902 to 57.2356 have been produced using the proposed technique. In particular, we achieved high NC values of 0.9801 on applying image processing attacks. A series of tests are performed on the system to determine its sensitivity, and the results are compared to protocols. The simulation results demonstrate that the suggested solution offers a high level of security while being imperceptible and robust to various attacks.

Data hiding in encryption–compression domain

This paper introduces a robust and secure data hiding scheme to transmit grayscale image in encryption-then-compression domain. First, host image is transformed using lifting wavelet transform, Hessenberg decomposition and redundant singular value decomposition. Then, we use appropriate scaling factor to invisibly embed the singular value of watermark data into the lower frequency sub-band of the host image. We also use suitable encryption-then-compression scheme to improve the security of the image. Additionally, de-noising convolutional neural network is performed at extracted mark data to enhance the robustness of the scheme. Experimental results verify the effectiveness of our scheme, including embedding capacity, robustness, invisibility, and security. Further, it is established that our scheme has a better ability to recover concealed mark than conventional ones at low cost.

Robust Secure Color Image Watermarking Using 4D Hyperchaotic System, DWT, HbD, and SVD Based on Improved FOA Algorithm

In the recent past, a different set of algorithms for watermarking and securing the color images have been developed by using transformation, decomposition, and optimization techniques for watermark embedding and extraction. In this paper, we propose an optimized and robust watermarking algorithm coupled with a 4D hyperchaotic system, and its performance is analyzed by extending and differentiating the existing work. Our contribution in the presented work is watermarking and securing the color images by an optimized algorithm that uses transformation technique such as Discrete Wavelet Transformation (DWT) and decomposition techniques such as Hessenberg decomposition (HbD) and singular value decomposition (SVD) coupled with the 4D hyperchaotic system, while the optimization is carried out by improved evolution fruit fly optimization algorithm (IEFOA). The experimental results based on different types of attacks (filter attacks, noise attacks, cropping attack, JPEG compression, motion blur, sharpening, and rotation), key sensitivity, normalized correlation, peak signal-to-noise ratio, and structural similarity index measure are done for measuring the algorithm’s performance regarding invisibility and robustness. The experimental results show that the proposed scheme has excellent invisibility and keeps a good trade-off between invisibility and robustness. The experiment results show that the proposed approach outperforms the previous approaches.

Blind Audio Watermarking Based on Parametric Slant-Hadamard Transform and Hessenberg Decomposition

Digital watermarking has been widely utilized for ownership protection of multimedia contents. This paper introduces a blind symmetric audio watermarking algorithm based on parametric Slant-Hadamard transform (PSHT) and Hessenberg decomposition (HD). In our proposed algorithm, at first watermark image is preprocessed to enhance the security. Then, host signal is divided into non-overlapping frames and the samples of each frame are reshaped into a square matrix. Next, PSHT is performed on each square matrix individually and a part of this transformed matrix of size m×m is selected and HD is applied to it. Euclidean normalization is calculated from the 1st column of the Hessenberg matrix, which is further used for embedding and extracting the watermark. Simulation results ensure the imperceptibility of the proposed method for watermarked audios. Moreover, it is demonstrated that the proposed algorithm is highly robust against numerous attacks. Furthermore, comparative analysis substantiates its superiority among other state-of-the-art methods.

An Optimized Image Watermarking Method Based on HD and SVD in DWT Domain

In this paper, a novel image watermarking method is proposed which is based on discrete wave transformation (DWT), Hessenberg decomposition (HD), and singular value decomposition (SVD). First, in the embedding process, the host image is decomposed into a number of sub-bands through multi-level DWT, and the resulting coefficients of which are then used as the input for HD. The watermark is operated on the SVD at the same time. The watermark is finally embedded into the host image by the scaling factor. Fruit fly optimization algorithm, one of the natural-inspired optimization algorithms is devoted to find the scaling factor through the proposed objective evaluation function. The proposed method is compared to other research works under various spoof attacks, such as the filter, noise, JPEG compression, JPEG2000 compression, and sharpening attacks. The experimental results show that the proposed image watermarking method has a good trade-off between robustness and invisibility even for the watermarks with multiple sizes.

Colour Image Blind Watermarking Scheme Based on Fast Walsh Hadamard Transform and Hessenberg Decomposition

Colour Image Blind Watermarking Scheme Based on Fast Walsh Hadamard Transform and Hessenberg Decomposition

A multimodal biometric watermarking system for digital images in redundant discrete wavelet transform

The traditional watermarking algorithms prove the rightful ownership via embedding of independent watermarks like copyright logos, random noise sequences, text etc into the cover images. Coupling biometrics with watermarking evolved as new and secure approach as it embeds user specific biometric traits and thus, narrows down the vulnerability to impostor attacks. A multimodal biometric watermarking system has been proposed in this paper in the redundant discrete wavelet transform(RDWT). Two biometric traits of the user i.e. the iris and facial features are embedded independently into the sub-bands of the RDWT of cover image taking advantage of its translation invariant property and sufficient embedding capacity. The ownership verification accuracy of the proposed system is tested based on the individual biometric traits as well as the fused trait. The accuracy was enhanced while using the fused score for evaluation. The security of the scheme is strengthened with usage of non-linear chaotic maps, randomization via Hessenberg decomposition, Arnold scrambling and multiple secret keys. The robustness of the scheme has been tested against various attacks and the verification accuracy evaluated based on false acceptance rate, false rejection rate, area under curve and equal error rate to validate the efficacy of the proposed scheme.