Embedding Factor Research Articles

Optimal frame selection-based watermarking using a meta-heuristic algorithm for securing video content

Optimal embedding factor selection is still an open challenging issue in video watermarking. To address the same, this paper introduces a modified gravitational search algorithm (MGSA) based video watermarking (VW) scheme, termed VW-MGSA. In this proposed method, a novel variant of gravitational search algorithm i.e MGSA is employed to attain multiple optimal embedding factors (MOEF). VW-MGSA embeds watermark logo into maximum entropy blocks of size 8 × 8 followed by 1-level RDWT and Schur transform. The proposed GSA variant (MGSA) was evaluated experimentally and statistically using 22 standard benchmark functions, covering unimodal, multimodal, and fixed-dimension categories. The performance has been assessed using key metrics such as mean, standard deviation, Friedman test, and convergence graphs. These results confirm that the proposed variant outperforms existing meta-heuristic algorithms. Moreover, VW-MGSA has been validated on 8 standard benchmark videos over 19 attacks and evaluated using PSNR, SSIM, and NC metrics. The experimental and statistical results confirm that VW-MGSA outperforms existing video watermarking methods. It significantly improves the balance between imperceptibility and robustness compared to existing methods, with a measured improvement of 39.63%. The improved performance of the VW-MGSA can be applied to real-world platforms like Netflix and Amazon Prime to safeguard licensed content, with watermarks aiding in tracing piracy sources.

An adaptive robust watermarking scheme based on chaotic mapping

Digital images have become an important way of transmitting information, and the risk of attacks during transmission is increasing. Image watermarking is an important technical means of protecting image information security and plays an important role in the field of information security. In the field of image watermarking technology, achieving a balance between imperceptibility, robustness, and embedding capacity is a key issue. To address this issue, this paper proposes a high-capacity color image adaptive watermarking scheme based on discrete wavelet transform (DWT), Heisenberg decomposition (HD), and singular value decomposition (SVD). In order to enhance the security of the watermark, Logistic chaotic mapping was used to encrypt the watermark image. By adaptively calculating the embedding factor through the entropy of the cover image, and then combining it with Alpha blending technology, the watermark image is embedded into the Y component of the YCbCr color space to enhance the imperceptibility of the algorithm. In addition, the robustness of the algorithm was further improved through singular value correction methods. The experimental results show that the average PSNR and SSIM of the watermarking scheme are 45.3437dB and 0.9987, respectively. When facing various attacks, the average NCC of the extracted watermark reaches above 0.95, indicating good robustness. The embedding capacity of this scheme is 0.6667bpp, which is higher than other watermarking schemes, and the average running time is 1.1136 seconds, which is better than most schemes.

An optimized watermarking scheme based on genetic algorithm and elliptic curve

Digital watermarking serves as a crucial tool for tracing copyright infringements and ensuring the authenticity and integrity of sensitive information. The fundamental concept involves embedding a watermark in the host information, ensuring its undetectability by unauthorized parties. The efficacy of a watermarking scheme mainly depends on achieving high levels of imperceptibility, robustness, and embedding capacity. These attributes are intricately linked to both the selection of the host information segment and the embedding factor. Existing schemes often (i) employ the entire host information for embedding, incurring computational expenses, and (ii) optimize the embedding factor without considering imperceptibility, robustness, and embedding capacity simultaneously, resulting in less secure watermarks. To address these limitations, we introduce a novel watermarking scheme leveraging elliptic curves (ECs) and genetic algorithms (GA). We model the choice of the embedding part by generating pseudo-random numbers over ECs, taking advantage of their proven sensitivity, security, and low computational complexity. Due to parallel search and adaptability to non-linear relationships of GA, the scheme employs genetic optimization with a multivariate objective function to establish a balance between imperceptibility, robustness, and embedding capacity for optimal watermarked generation. Rigorous analysis and comparisons demonstrate that our proposed scheme attains significantly higher imperceptibility, robustness, and embedding capacity compared to existing optimized schemes. Furthermore, our scheme exhibits a speed advantage, being up to 278 and 21 times faster than optimized and non-optimized schemes, respectively, thereby affirming its practical applicability.

A Robust Dual Watermarking using Grey Wolf Optimization, Selective Encryption and Fast Flexible De-Noising Convolution Neural Network

Digital data interchange in IoT systems has flourished with the advancement of industrial internet technologies. Particularly, more and more digital images created by intelligent and industrial equipment are sent there are security concerns related to the website, server, and cloud. To accomplish this issue, in this article a secure watermarking approach is suggested in this research to effectively improve security, invisibility, and resilience at the same time. The adequate coefficient for information embedding is first determined using an assortment of transform domain techniques Discrete-Wavelet-Transform (DWT), Heisenberg- decomposition (HD), and Tensor-singular-value-decomposition (T-SVD). Using the grey wolf optimization (GWO) approach, we estimated the appropriate embedding factors to provide a reasonable compromise between robustness and invisibility. To enable the suggested approach to offer an additional level of security, a selective encryption technique is used on the watermark image. Moreover, FFDNet—a quick and adaptable de-noising convolutional-neural–network is working to increase the robustness-of-the suggested algorithm. The results demonstrate that the recommended watermarking method generates exceptional imperceptibility, resilience, and security against standard attacks. Additionally, the comparison demonstrates that the suggested algorithm performs better than alternative strategies. The following metrics were reached: 51.6966 dB, 0.9944, 0.9961, and 0.2849 for the peak-signal- to-noise ratio (PSNR), Structural-Similarity-Index (SSIM), number of changing pixels per second (NPCR), and unified-averaged-changed-intensity (UACI) average scores.

A Hermitian refinement of symplectic Clifford analysis

In this paper, we develop the Hermitian refinement of symplectic Clifford analysis, by introducing a complex structure on the canonical symplectic manifold . This gives rise to two symplectic Dirac operators and (in the sense of Habermann), leading to a ‐invariant system of equations on . We discuss the solution space for this system, culminating in a Fischer decomposition for the space of (harmonic) polynomials on with values in the symplectic spinors. To make this decomposition explicit, we will construct the associated embedding factors using a transvector algebra.

The electrically conducting water-based nanofluid flow containing titanium and aluminum alloys over a rotating disk surface with nonlinear thermal radiation: A numerical analysis

Abstract A metallic alloy is a combination of two or more elements, often called a compound or a solution. Steel is largely composed of carbon, a nonmetal, but alloys are often made up of metals. In this article, the authors have explored the electrically conducting water-based viscous nanofluids flow past a rotating disk surface. The nanofluids flow is composed of titanium and aluminum alloys where water is used as a base fluid. Two important cases, namely the stretching case and the shrinking case, were investigated to analyze the flow behaviors due to the different embedding factors. The impacts of viscous Joule heating, thermophoresis, Brownian motion, activation energy, nonlinear thermal radiation, and chemical reaction are investigated here. By employing an appropriate set of variables for shifting the leading equations to dimension-free form. The mathematical model is solved numerically by incorporating the bvp4c MATLAB scheme. Current work is validated with previous studies. The outcomes showed that the radial velocity increases when the disk surface stretches and reduces when the disk surface shrinks. On the other hand, the Azimuthal velocity increases when the disk surface shrinks and reduces when disk surface stretches. Both the radial and Azimuthal velocities are the diminishing functions of the magnetic factor, whereas temperature is the growing function of magnetic factor. In addition, the temperature is more influenced by the magnetic factor in the case of nonlinear radiation. The higher magnetic factor increases skin friction. In addition, the stretching case experiences more surface drag than the shrinking case. It is found that nanofluid flow containing titanium alloy has perceived the greater impacts of the embedded factors compared to the nanofluid flow containing aluminum alloy.

Evolution of values in China: Disembedding factors and structural transformation

This study, guided by Taylor’s Great Disembedding Theory, examines global and Chinese societal values through the World Values Survey’s four-wave data from 1994 to 2014. Factor analysis shows six core values as disembedding factors (friends, leisure, politics) and embedding factors (family, work, religion). These data collected during China’s one-child policy and economic reforms reveal the country’s value shifts: leisure gains importance while politics is more moderate. This disembedding trend suggests a growing demand for social services driven by an emphasis on life balance. These evolving values guide social workers in tailoring programs to meet diverse needs shaped by personal and societal expectations.

EEG-Based Driver Mental Fatigue Recognition in COVID-19 Scenario Using a Semi-Supervised Multi-View Embedding Learning Model

With the spread of COVID-19 in recent years, wearing masks has increased the difficulty of driver mental fatigue recognition. Electroencephalogram (EEG) signal has become an important physiological signal index to reflect the driver’s mental state. However, the drivers’ EEG data is plagued by inadequate labels and multi-view data, which makes classification difficult. To solve this problem, this study proposes a semi-supervised multi-view sparse regularization and graph embedding learning (SMSG) model. To obtain discriminative feature representations of semi-supervised EEG data, SMSG fully mines diverse information from multiple views based on sparse regularization embedding and graph embedding technology. SMSG employs the graph embedding to capture the discriminative structure and local manifold structure on multi-view data. Furthermore, SMSG learns the common shared regularization embedding and private regularization embedding factors to preserve the consistency and diversity of the multi-view data. Through self-adaptive learning, the weights of each view can be directly solved adaptively. This works also introduces kernel trick to project the SMSG model into the nonlinear reproducing kernel Hilbert space (RKHS), which can obtain more approximate EEG feature representation. Experiments on the real dataset verify the effectiveness of the SMSG model for EEG-based driver mental fatigue recognition.

A CPC fractional model of the heat and mass transport mechanism in Carbon nanotubes with slip effects on velocity

A fractional technique is used to evaluate the temperature, mass, and velocity flow of single and double wall CNTs over a vertical plate. Slip boundary conditions and applied magnetic force are addressed. Human blood is used to examine how base fluid behaves. Applying the proper dimensionless variables results in the dimensionless formulation of initial and boundary conditions related to the governed dimensional concentration, momentum, and energy equations. The Laplace transform technique is used to resolve the dimensionless governing partial differential equations and get the solutions. The constant proportional Caputo (CPC) time-fractional derivative is a unique class of fractional model used in the simulation technique. The fundamental definitions are used to support the said model first. Using MWCNTs and SWCNTs in comparison to the flow characteristics, a thermal and mass study is given. The heat and mass transfer processes for single-walled carbon nanotubes (SWCNTs) have been shown to typically be progressive. The momentum profile decreases as the fractional variables rise. Multi-walled carbon nanotubes (MWCNTs) show more progressive velocity control as a result of the magnetic parameter. Graphs demonstrate the influence of embedding factors on the velocity, energy, and concentration profiles.

NEFM: Neural embedding based factorization machines for user response prediction

As Factorization Machine (FM) models linearly describe feature interactions, they cannot accurately capture complex non-linear features of data. Furthermore, random initialization in these FM models seriously affects system convergence and performance. Therefore, the random embedding process of FM models may not be sufficient to capture the data information. Although deep neural networks (DNNs)-based FM models have been recently proposed for advanced feature interactions, it is difficult to train. To address these challenges, we propose a neural embedding factorization machine (NEFM) model, which effectively initializes the embedding layers based on an unsupervised pre-training framework of probabilistic auto-encoder. The NEFM smartly couples the good linearity of FM models in modeling second-order feature interactions and the advantage of DNNs in modeling non-linear feature interactions. Experimental results show the effectiveness of the proposed NEFM. For example, the performance of NEFM is enhanced by at least 6.99% than the non-pre-trained FM models. Compared with the pre-trained FM by DNNs-based models, the NEFM model reduces at least 1.02% in test errors.

Optimal blind colour image watermarking based on adaptive chaotic grasshopper optimization algorithm

ABSTRACT This paper proposes an effective optimal blind colour image watermarking based on Triangular Vertex Transform (TVT), Lifting Wavelet Transform (LWT) and Schur decomposition. Firstly, the RGB channels of the colour host image are processed with TVT to attain , and coefficients. The image quality is preserved by applying LWT on the coefficients. As the middle frequency (HL and LH) components of the LWT wavelet domain achieve much robustness, these two components are split into nonoverlapping blocks further it is processed with Schur decomposition for improving perceptual transparency. The optimal solution attained by an Adaptive Chaotic Grasshopper Optimization Algorithm (ACGOA) is used as the embedding factor to ensure the embedding strength of the watermark. The confidentiality of the watermark is improved by 2D Logistic-Modulated-Sine-Coupling-Logistic Chaotic Map (LSMCL). Subsequently, each bit of encrypted watermark image is inserted in the highest eigenvalue of upper triangular matrix of Schur decomposition using the embedding factor β obtained by ACGOA to accomplish the watermarked image. The watermark recovery is performed using the embedding factor β and the decryption process is done using LSMCL. The efficiency of the ACGOA based scheme is estimated and the highest Peak signal-to-noise ratio (PSNR) value achieved is 54.2987 dB. The Normalized Cross-Correlation (NCC) results show better reliability for different attacks including geometric attacks and the NCC value obtained is close to 1.

Linked cluster expansions via hypergraph decompositions.

We propose a hypergraph expansion which facilitates the direct treatment of quantum spin models with many-site interactions via perturbative linked cluster expansions. The main idea is to generate all relevant subclusters and sort them into equivalence classes essentially governed by hypergraph isomorphism. Concretely, a reduced König representation of the hypergraphs is used to make the equivalence relation accessible by graph isomorphism. During this procedure we determine the embedding factor for each equivalence class, which is used in the final resummation in order to obtain the final result. As an instructive example we calculate the ground-state energy and a particular excitation gap of the plaquette Ising model in a transverse field on the three-dimensional cubic lattice.

SecDH: Security of COVID-19 images based on data hiding with PCA

Nowadays, image security and copyright protection become challenging, especially after the COVID-19 pandemic. In the paper, we develop SecDH as a medical data hiding scheme, which can guarantee the security and copyright protection of the COVID-19 images. Firstly, the cover image is normalized, which offers high resistance against the geometric attacks. Secondly, the normalized principal component as embedding factor is computed, which are calculated based on principal component analysis (PCA) between cover and mark image. Thirdly, the medical image is invisibly marked with secret mark based on normalized component, redundant discrete wavelet transform (RDWT) and randomized singular value decomposition (RSVD) is introduced. Finally, Arnold cat map scheme employed to ensure the security of the watermarking system. Under the experimental evaluation, our SecDH tool is not only imperceptible, but also has a satisfactory advantage in robustness and security compared with the traditional watermarking schemes.

Digital Watermarking Using Machine Learning

Abstract: Digital watermarking is a technique used for the information of the images that provides security for the confidentiality. The repetitions of the multimedia objects (i.e. audio, video, text, etc.) have been protected by some of the developed digital watermarking techniques. Digital Watermarking is the process of concealing messages in digital contents in order to verify the rightful owner of the copyright protection. In this paper we have proposed a method that would assist its users to embed a watermark to the cover image based on an adaptive approach in a much robust way while maintaining the quality of the cover image. The implementation of this algorithm is based upon cascading the features of DWT and PCA using Bhattacharyya distance and Kurtosis. PCA decompose and compress the watermark, which results in better PSNR and NCC values for the tested images. The proposed algorithm uses Bhattacharyya distance and Kurtosis to detect the scaling and embedding factors making it adaptive to the input image rather than providing constant value. Also, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture, learning algorithm, and regularization method into image denoising. Specifically, residual learning and batch normalization are utilized to speed up the training process as well as boost the performance. Keywords: Digital watermarking, DWT-PCA, PSNR, Image denoising, convolutional neural network, DnCNN, residual learning, batch normalization.

Optimal Colour Image Watermarking Using Neural Networks and Multiobjective Memetic Optimization

This paper deals with the problem of robust and perceptual logo watermarking for colour images. In particular, we investigate trade-off factors in designing efficient watermarking techniques to maximize the quality of watermarked images and the robustness of watermark. With the fixed size of a logo watermark, there is a conflict between these two objectives, thus a multiobjective optimization problem is introduced. We propose to use a hybrid between general regression neural networks (GRNNs) and multiobjective memetic algorithms (MOMA) to solve this challenging problem. Specifically, a GRNN is used for efficient watermark embedding and extraction in the wavelet domain. Optimal watermark embedding factors and the smooth parameter of the GRNN are searched by a MOMA for optimally embedding watermark bits into wavelet coefficients. The experimental results show that the proposed approach achieves robustness and imperceptibility in watermarking.

Fingerprint-based robust medical image watermarking in hybrid transform.

To protect the medical images integrity, digital watermark is embedded into the medical images. A non-blind medical image watermarking scheme based on hybrid transform is propounded. In this paper, fingerprint of the patient is used as watermark for better authentication, identifying the original medical image and privacy of the patients. In this scheme, lifting wavelet transform (LWT) and discrete wavelet transform (DWT) are utilized for amplifying the watermarking algorithm. The scaling and embedding factors are calculated adaptively with the help of Local Binary Pattern values of the host medical image to achieve better imperceptibility and robustness for medical images and fingerprint watermark, respectively. Two-level decomposition is done where for the first level LWT is utilized and for the second level decomposition DWT is utilized. At the extraction side, non-blind recovery of fingerprint watermark is performed which is similar to the embedding process. The propounded design is implemented on various medical images like Chest X-ray, CT scan and so on. The propounded design provides better imperceptibility and robustness with the combination of LWT–DWT. The result analysis proves that the proposed fingerprint watermarking scheme has attained best results in terms of robustness and authentication with different medical image attacks. Peak Signal to Noise Ratio and Normalized Correlation Coefficient metrics are used for evaluating the proposed scheme. Furthermore, superior results are obtained when compared to related medical image watermarking schemes.

Improving and embedding project management practice: generic or context dependent?

Taking into account the contingency theory, this paper explores the extent to which key project management improvement initiatives and key embedding factors, identified in a previously developed conceptual framework, are dependent on organizational context, namely sector of activity, organization size, geographic area and project types. Therefore, aiming to guide professionals on making use of such framework in their organizations. Statistically significant contextual correlations were looked for in a worldwide sample of 793 questionnaire responses from project management professionals, using Principal Component Analysis, ANOVA test and post-hoc Tukey test. Context related differences found were limited, suggesting that the framework for improving and embedding project management practice is substantially generic. Therefore, the paper shows the explanatory power of the framework, which can be used by any organization independent of its sector of activity, dimension, geographic area and project types, however indicating the existence of slight differences. For example, Information Technology companies might give more relevance to initiatives such as corporate standardization and tailoring of project management processes tools and techniques than Engineering and Construction companies.

Multiplicative Watermarking Method with the Visual Saliency Model Using Contourlet Transform

We have proposed an image adaptive watermarking method by using contourlet transform. Firstly, we have selected high-energy image blocks as the watermark embedding space through segmenting the original image into nonoverlapping blocks and designed a watermark embedded strength factor by taking advantage of the human visual saliency model. To achieve dynamic adjustability of the multiplicative watermark embedding parameter, the relationship between watermark embedded strength factor and watermarked image quality is developed through experiments with the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM), respectively. Secondly, to detect the watermark information, the generalized Gaussian distribution (GGD) has been utilized to model the contourlet coefficients. Furthermore, positions of the blocks selected, watermark embedding factor, and watermark size have been used as side information for watermark decoding. Finally, several experiments have been conducted on eight images, and the results prove the effectiveness of the proposed watermarking approach. Concretely, our watermarking method has good imperceptibility and strong robustness when against Gaussian noise, JPEG compression, scaling, rotation, median filtering, and Gaussian filtering attack.

Selective encryption and optimization based watermarking for robust transmission of landslide images

In this paper, a secure watermarking algorithm is proposed to efficiently enhance invisibility and robustness simultaneously. First, the combination of transform domain scheme is used to identify the appropriate coefficient for data embedding. To offer a good balance between robustness and invisibility, we computed optimal embedding factors by using the fusion of particle swarm optimisation (PSO) and the Firefly algorithm. Subsequently, the watermark is scrambled by using a step space-filling curve, and the scrambled watermark is embedded into the wavelet domain of the cover image via the optimal embedding factor. Furthermore, selective encryption is applied to the original host image so that the proposed algorithm can also provide an additional level of security at low cost. We achieved average scores of peak signal-to-noise ratio (PSNR), normalised coefficient (NC), number of changing pixel rate (NPCR) and unified averaged changed intensity (UACI) as 55.1471 dB, 0.9956, 0.9801 and 0.3471, respectively.

Hybrid Watermarking Scheme with Dual Encryption and Channel Coding in YCbCr Color Space

Objectives: This study aims to Improve and Secure the watermarking scheme with dual encryption (chaotic maps and Arnold transform) and channel coding in YCbCr color space with embedding and extraction procedure. Methods: In this scheme, the cover watermarked image is encoded and the singular value is decomposed by singular value decomposition (SVD). The four levels of Discrete Wavelet Transform (DWT) are applied after that, the singular value matrixes are embedded into the Y, Cb, Cr components of the host image.The embedding factor for each component is calculated with singular vectors of the hl sub-band of DWT with bit selection automatically by Gray level co-occurrence matrix (GLCM). In this paper, the GLCM technique is used to enhance the performance of a watermarked image affected by degradation with the DWT method. An inefficient approach is chosen randomly for image embedding which is bit selection. There is degradation in the quality of the watermark image when randomly selecting the bits. To dynamically choose the embedding bit, this research applies the Grey Level Co-occurrence Matrix method. Findings: Different performance parameters like Mean Squared Error (MSE), Peak Signal to Noise ratio (PSNR), Bit Error Rate (BER), Normalized correlation coefficient (NCC), and Mean Structural Similarity Index Measure (MSSIM) has been used to compare the effectiveness of the proposed scheme. The achieved outcomes show that when applying dual encryption and FFT (Fast Fourier Transform) with the GLCM, around 10 to 15 percent improvement in the results can be obtained. Novelty: We have proposed a hybrid watermarking scheme with Chaotic maps, Arnold transform and Fast Fourier transform in YCbCr color space. Keywords: Encryption; Discrete wavelet transform; Singular value decomposition; Chaotic map; Arnold transforms; Channel coding