Search

This paper marks the initial phase in the development of the Attachment Defenses Questionnaire (ADQ-50), a self-report tool crafted to assess defense mechanisms associated with attachment processes, catering to both clinical and research contexts. Anchored in the theoretical framework of attachment theory, the ADQ posits that an individual's internalized attachment style plays a influential role in predicting their defense mechanisms. The paper outlines the comprehensive development and refinement process of the ADQ-50. In Study 1 a preliminary 176-item version of the ADQ was examined. Data was collected online drawing from participants sourced from Prolific and undergraduate students (N = 1994). Study 2 further refined the ADQ, evaluating its initial convergent validity with a diverse participant pool (N = 726), including undergraduates, Prolific contributors, general practice medical patients, and individuals from social media. Exploratory factor analysis revealed a robust ten-factor structure, resulting in a 50-item scale aligning with theoretical expectations and demonstrating good psychometric properties. Findings, limitations, strengths and future research directions are discussed. We posit that the ADQ holds great potential to deepen our comprehension of defense mechanisms linked to attachment, with wide-ranging implications for clinical practices. [ABSTRACT FROM AUTHOR]

Watermarking is embedding visible or invisible data within media to verify its authenticity or protect copyright. The watermark is embedded in significant spatial or frequency features of the media to make it more resistant to intentional or unintentional modification. Some of these features are important perceptual features according to the human visual system (HVS), which means that the embedded watermark should be imperceptible in these features. Therefore, both the designers of watermarking algorithms and potential attackers must consider these perceptual features when carrying out their actions. The two roles will be considered in this paper when designing a robust watermarking algorithm against the most harmful attacks, like volumetric scaling, histogram equalization, and non-conventional watermarking attacks like the Denoising Convolution Neural Network (DnCNN), which must be considered in watermarking algorithm design due to its rising role in the state-of-the-art attacks. The DnCNN is initialized and trained using watermarked image samples created by our proposed Covert and Severe Attacks Resistant Watermarking Algorithm (CSRWA) to prove its robustness. For this algorithm to satisfy the robustness and imperceptibility tradeoff, implementing the Dither Modulation (DM) algorithm is boosted by utilizing the Just Noticeable Distortion (JND) principle to get an improved performance in this sense. Sensitivity, luminance, inter and intra-block contrast are used to adjust the JND values. [ABSTRACT FROM AUTHOR]

This study presents a robust and imperceptible image watermarking method combining Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and quantum-inspired optimization. The approach applies DWT to decompose the image into subbands and embeds the watermark in the low-frequency subband. DCT is used to identify Alternating Current (AC) coefficients, with Quantum-Inspired Annealing (QIA) optimizing their selection and Quantum Variational Circuits (QVC) dynamically adjusting the embedding intensity (α) for each block. The novelty of this study lies in integrating QIA and QVC to optimize both the embedding position and intensity, enabling a more adaptive and robust watermarking mechanism compared to existing quantum-inspired methods. Experimental results in standard images show high imperceptibility, with average Peak signal-to-noise ratio (PSNR) and Structural Similarity Index Measurement (SSIM) values of 46.93 dB and 0.9979, respectively. The method demonstrates strong robustness, achieving an average Normalized Correlation (NC) of 0.9752 across various attacks, including JPEG compression, noise addition, and cropping. Compared to existing methods, the proposed approach performs better in maintaining watermark quality and robustness. This study highlights the potential of quantum-inspired techniques in watermarking, offering a promising direction for further research and real-world applications. [ABSTRACT FROM AUTHOR]

ABSTRACT In this paper, the equivalence canonical forms of two sets of five quaternion matrices are considered. The simultaneous decompositions of these two sets of quaternion matrices are established. Some practical algorithms for computing the decompositions are also presented. Using the equivalence canonical forms, some practical solvability conditions for two systems of generalized Sylvester‐type quaternion matrix equations are derived in terms of the ranks of the coefficient matrices. The general solutions to the systems are also provided. Some numerical examples are given to illustrate the results. The proposed simultaneous decompositions are applied to construct a new framework of color watermarks embedding. In this framework, five color watermarks can be embedded into one host image simultaneously with high efficiency. Moreover, a counterexample for the existence of the equivalence canonical form of the general matrix quaternity with the same row or column numbers is presented. The results extend the existing findings on the equivalence canonical forms of general matrix arrays. [ABSTRACT FROM AUTHOR]

Electrospinning of polymer material has gained a lot of interest in the past decades. Various methods of electrospinning have been applied for different applications, from needle electrospinning to needleless electrospinning. A relatively new variation of electrospinning, namely near-field electrospinning, has been used to generate well-defined patterns. This variation of electrospinning, also known as near-field direct-write electrospinning, allows for precise control of the fiber deposition, sacrificing on the thickness of the resulting fibers. Typically, for this method, melt electrospinning is preferred, since it provides a higher viscosity of the polymer and thereby better control of the fiber deposition. However, when mixing additives into the spinning dope, a solution spinning approach is preferable since it provides a more homogeneous distribution of the additives in the spinning dope. A fluorescent spinning dope of dissolved PET with fluorescent carbon quantum dots has been used to generate the fluorescent patterns. These can be used to generate logos, bar codes, or QR codes to encode information about the material, such as watermarks or counterfeiting tags. [ABSTRACT FROM AUTHOR]

The rapid advancement of Artificial Intelligence Generated Content (AIGC) has significantly accelerated the evolution of Deepfake technology, thereby introducing escalating social risks due to its potential misuse. In response to these adverse effects, researchers have developed defensive measures, including passive detection and proactive forensics. Although passive detection has achieved some success in identifying Deepfakes, it encounters challenges such as poor generalization and decreased accuracy, particularly when confronted with anti-forensic techniques and adversarial noise. As a result, proactive forensics, which offers a more resilient defense mechanism, has garnered considerable scholarly interest. However, existing proactive forensic methodologies often fall short in terms of visual quality, detection accuracy, and robustness. To address these deficiencies, we propose a novel proactive forensic approach that utilizes pseudo-Zernike moment robust watermarking. This method is specifically designed to enhance the detection and analysis of face swapping by transforming facial data into a binary bit stream and embedding this information within the non-facial regions of video frames. Our approach facilitates the detection of Deepfakes while preserving the visual integrity of the video content. Comprehensive experimental evaluations have demonstrated the robustness of this method against standard signal processing operations and its superior performance in detecting Deepfake manipulations. [ABSTRACT FROM AUTHOR]

Watermarking methods have traditionally emerged from the imperative to guarantee the authenticity of digital documents. As we navigate the current landscape of advancing voice imitation and forgery techniques, safeguarding forensic evidence signals, historic speeches, and music signals has become an urgent challenge. A notable concern with existing methods is the presence of blind spots—areas where tampering goes undetected—significantly impacting detection sensitivity and rates. To address these challenges, this research introduces a fragile audio watermarking method designed to identify even the slightest modifications in audio signals. The approach relies on embedded nested SHA-256 hashes. Performance evaluation demonstrates that any inversion of a bit in the watermarked signals is promptly detected on the blind side of detection. Notably, blind spots are minimized, leading to a remarkable 100% detection rate. Furthermore, comparative results highlight the lightweight nature of the watermark, ensuring high signal imperceptibility. [ABSTRACT FROM AUTHOR]

Massive databases encounter security such as data theft, illegal copying, and copyright infringement during creating, transmitting, and sharing of big data. The reversible data watermarking technology can effectively solve these problems, which can extract the watermark information accurately and recover the original carrier data without any distortion. However, most existing methods extract watermark information non-blindly and cannot effectively achieve a balance between watermark embedding capacity and data distortion. This paper proposes a blind reversible database watermarking method based on dual embedding, which combines histogram shifting and distortion-free watermarking methods to achieve an adaptive selection of histogram bins, blind extraction of watermark information, and carrier data recovery. The proposed method preprocesses the database tuples by scrambling them and constructs a prediction error histogram using first-layer tuples in square prediction within each group. The watermark information is embedded through adaptive selection and expansion of histogram bins, while the distortion-free watermarking method is used in another layer to assist in the recovery of original carrier data. The experimental results show that the proposed method can achieve an embedding capacity of more than three times the capacity of existing methods. It can also achieve blind watermark extraction and outperform some other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Symmetry-breaking in security mechanisms can create vulnerabilities which attackers may exploit to gain unauthorized access or cause data leakage, ultimately compromising the integrity and security of vector geographic data. How to achieve tamper localization remains a challenging task in the field of data authentication research. We propose a precise authentication watermarking algorithm for vector geographic data based on multiple sorting mechanisms. During the watermark embedding process, a sequence of points is initially extracted from the original data, followed by embedding watermarks into each coordinate point. The embedded watermark information consists of the self-identification and ordering information of each coordinate point. Ordering information is crucial for establishing relationships among points and enhancing tamper localization. During the authentication phase, the extracted watermark information is compared with the newly generated watermark information. Self-identification information is used to authenticate addition attacks, while ordering information is used to authenticate deletion attacks. Experimental results demonstrate that the proposed algorithm achieves high precision in detecting and localizing both addition and deletion attacks, significantly outperforming the comparison method. [ABSTRACT FROM AUTHOR]

Statement of Retraction: We, the Editor and Publisher of Applied Artificial Intelligence, have retracted the following article: Zhang Maoyun, Xi Huizhuang, Tang Chen, Jiang Yuheng, Zhang Ziyan & Tian Chunlin, Binding Mechanism of Aviation Wire Harness Based on Improved Particle Swarm Optimization, Applied Artificial Intelligence Since publication, concerns have been raised by the authors about the integrity of the data presented in the article. The authors have identified issues with the programming statistics that have led to conclusions which are not credible and have requested the retraction of the article. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as "Retracted". [ABSTRACT FROM AUTHOR]

Weave and drainage marks on paper surfaces derived from forming fabrics installed in paper machines were examined. Non-destructive differentiation of copy papers from various manufacturers was performed by a 2D Lab Formation Sensor. Individual characteristics such as angles and wavelengths formed by the fabric were selected as parameters for the analysis. These parameters were sampled from a total of 500 papers: 50 sheets of paper from each of the 10 groups by copy paper manufacturers. Using machine learning algorithms such as partial least squares discriminant analysis (PLS-DA), k-nearest neighbors (k-NN), and naive bayes (NB) models to analyze these parameters allows for the identification of copy paper groups by manufacturers. The NB classifier demonstrated superior performance, achieving an accuracy of 0.973 even with a 50 % reduction in input variables. This study shows that periodic marks on paper surfaces can act as important indicators of the forming fabric and the distinct features of a paper-making machine. In general, this paper proposes that the 2D Lab Formation Sensor along with machine learning models can be utilized for non-destructive copy paper categorization. This identification method could be broadened to determine information regarding suspicious documents for forensic identification purposes. [ABSTRACT FROM AUTHOR]

Recent advancements in deep neural networks (DNNs) have made them indispensable for numerous commercial applications. These include healthcare systems and self-driving cars. Training DNN models typically demands substantial time, vast datasets and high computational costs. However, these valuable models face significant risks. Attackers can steal and sell pre-trained DNN models for profit. Unauthorised sharing of these models poses a serious threat. Once sold, they can be easily copied and redistributed. Therefore, a well-built pre-trained DNN model is a valuable asset that requires protection. This paper introduces a robust hybrid two-level protection system for safeguarding the ownership of pre-trained DNN models. The first-level employs zero-bit watermarking. The second-level incorporates an adversarial attack as a watermark by using a perturbation technique to embed the watermark. The robustness of the proposed system is evaluated against seven types of attacks. These are Fast Gradient Method Attack, Auto Projected Gradient Descent Attack, Auto Conjugate Gradient Attack, Basic Iterative Method Attack, Momentum Iterative Method Attack, Square Attack and Auto Attack. The proposed two-level protection system withstands all seven attack types. It maintains accuracy and surpasses current state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Digital watermarking conceals data within host images to safeguard against unauthorized distribution of multimedia content. It offers content protection and anti-piracy measures, maintaining content quality by embedding invisible information. This process involves inserting and extracting watermarks. We introduce a robust algorithm, combining discrete Haar wavelet transform (DHWT) and discrete cosine transform (DCT), yielding effective watermarking with high resistance to extraction without data loss. This combination of transforms represents a hybrid approach that we call HyDHWCT in this work. Evaluations reveal our approach's superior accuracy compared to state-of-the-art methods. Our hardware watermarking solution excels in robustness and energy efficiency, even under diverse attack scenarios. FPGA and ASIC assessments show our HyDHWCT's exceptional area and power performance, with the algorithm achieving a lossless watermark extraction (NC = 1), outperforming prior methods in accuracy-quality, and energy-, and area-savings (approximately 2.621 × and 1.174 × , respectively). Accuracy-quality results confirm a perfect extraction rate (NC = 1), ensuring 100% accuracy in watermark extraction. [ABSTRACT FROM AUTHOR]

Copyright of LHB: Hydroscience Journal is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, a watermarking technique based on discrete curvelet transform and discrete cosine trans- form is proposed to protect the color document images. The six layers of the document image are created using the Discrete Curvelet Transform (DCuT). The sixth layer is chosen for the embedding process, while the remaining layers are discarded. Discrete Cosine Transform (DCT) is applied on the 8 × 8 blocks of the real part of sixth layer. DCT coefficients from low to medium are selected for the embedding process, excluding the first one. To choose the Direct Current (DC) coefficients of DCT to embed the watermark bits, a zigzag function is used. The same process is applied on the receiver side watermark image is extracted. Since this is a non-blind method, we require both a cover image and a watermarked image. DCT of watermarked image and the cover image are compared to extract the watermark image. The resistivity of the watermarked image against image processing attacks is measured using the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Mean Square Error (MSE), Normalization Correlation (NC), Bit Error Rate (BER) and Universal Quality Index (UQI). [ABSTRACT FROM AUTHOR]

Better understanding the effects of hurricane waves and storm surges on barrier-island breaching is important for both scientific research and coastal hazard mitigations. In this study, the 2D non-hydrostatic Xbeach model has been applied to investigate interactions of hurricane wave, storm surge, and morphological processes in the case study of St. Joseph Peninsula during Category 5 Hurricane Michael. Model validations show a 2.45% average error and the 0.88 skill score between modeled and observed high water marks and bed elevations, respectively. Analysis of spatial distributions of currents and water levels indicates that a narrow area was overtopped at peak storm surge and wave. The gap was then quickly enlarged as the breaching area by wave-surge actions. By investigating foredune and peak dune along the central axis of breaching area, it shows that the foredune erosion on the ocean-side by wave-surge-current directly lead to the breach of the peak dune area in the barrier island. The Froude number shows a strong correlation with quick erosion of the barrier, indicating wave-surge supercritical flow is one of the major factors causing the barrier breaching. Results of cross sections of bed elevations and instantaneous surge-wave profiles at different storm surge stages reveal the evolution of the barrier-island breach. Results from this study provide valuable references for coastal hazard mitigation and resilience communities. Highlights: Application of non-hydrostatic Xbeach model reveals barrier-island breaching process and wave-surge-barrier interactions. Model validations show a 2.45% average error and the 0.88 skill score for high water marks and bed elevations, respectively. The breach started with a narrow gap overtopped in the peak of storm surge and was then quickly enlarged by wave-surge actions. Wave-surge induced supercritical flow is one of the major factors accelerating the barrier breaching. Foredune erosion by wave-surge-current is another factor that accelerates the breach of the barrier island. [ABSTRACT FROM AUTHOR]

Traditional anti-counterfeiting techniques and methods are inadequate to meet the modern, sophisticated anti-counterfeiting requirement in banknotes. We applied our homemade near-field scanning microwave microscopy (NSMM) for penetrating imaging of concealed anti-counterfeiting features in banknotes. This NSMM imaged and characterized several vital concealed anti-counterfeiting features in the banknotes, including the dynamic color-shifting security threads, color-changing denomination numbers, magnetically embedded security threads, and white watermarks. At the same time, with the obstruction of red ink, near-field microwaves still penetrated the medium layer to characterize the detailed information of the security threads successfully. Additionally, the cavity perturbation method was used to obtain the effective permittivity of 3.65 and 3.62 for the windowing security thread and middle-embedded security thread. The thickness of the watermark was calculated to be about 3.3 μm with the calibrated fit method. [ABSTRACT FROM AUTHOR]

The tasks assigned to neural network (NN) models are increasingly challenging due to the growing demand for their applicability across domains. Advanced machine learning programming skills, development time, and expensive assets are required to achieve accurate models, and they represent important assets, particularly for small and medium enterprises. Whether they are deployed in the Cloud or on Edge devices, i.e., resource-constrained devices that require the design of tiny NNs, it is of paramount importance to protect the associated intellectual properties (IP). Neural networks watermarking (NNW) can help the owner to claim the origin of an NN model that is suspected to have been attacked or copied, thus illegally infringing the IP. Adapting two state-of-the-art NNW methods, this paper aims to define watermarking procedures to securely protect tiny NNs' IP in order to prevent unauthorized copies of these networks; specifically, embedded applications running on low-power devices, such as the image classification use cases developed for MLCommons benchmarks. These methodologies inject into a model a unique and secret parameter pattern or force an incoherent behavior when trigger inputs are used, helping the owner to prove the origin of the tested NN model. The obtained results demonstrate the effectiveness of these techniques using AI frameworks both on computers and MCUs, showing that the watermark was successfully recognized in both cases, even if adversarial attacks were simulated, and, in the second case, if accuracy values, required resources, and inference times remained unchanged. [ABSTRACT FROM AUTHOR]

The absence of validation or comparison data for verifying flood mapping methods poses a significant challenge in developing operational hydraulic approaches. This article aims to address this gap by presenting a benchmark dataset for flash flood mapping in the French Mediterranean region. The dataset described in this paper (Nicolle et al., 2024) includes flood hazard maps and simulation results of three actual flash flood events, all computed in steady regime at a 5-meter resolution using a 2D SWE model (neglecting inertia) named Floodos (Davy et al., 2017). Additionally, it includes the input data necessary (Digital Terrain Models, inflow discharges, hydrographic network) for conducting similar simulations with other hydrodynamic modeling approaches, in both steady and unsteady regimes. A comprehensive validation dataset, comprising observed flood extents, high water marks, and rating curves, is also provided, enabling a detailed evaluation of 2D hydraulic simulation results. The simulation results from Floodos, compared against stage-discharge rating curves available at gauging stations, yielded highly encouraging outcomes. The median error (sim. - obs.) was -0.04 m for the 2-year return period and -0.14 m across all simulated return periods, ranging from 2 to 1000 years. [ABSTRACT FROM AUTHOR]

Digital documents play a crucial role in contemporary information management. However, their quality can be significantly impacted by various factors such as hand-drawn annotations, image distortion, watermarks, stains, and degradation. Deep learning-based methods have emerged as powerful tools for document enhancement. However, their effectiveness relies heavily on the availability of high-quality training and evaluation datasets. Unfortunately, such benchmark datasets are relatively scarce, particularly in the domain of Traditional Chinese documents. We introduce a novel dataset termed "Joint Variation and ZhuYin dataset (JVZY)" to address this gap. This dataset comprises 20,000 images and 1.92 million words, encompassing various document degradation characteristics. It also includes unique phonetic symbols in Traditional Chinese, catering to the specific localization requirements. By releasing this dataset, we aim to construct a continuously evolving resource explicitly tailored to the diverse needs of Traditional Chinese document enhancement. This resource aims to facilitate the development of applications that can effectively address the challenges posed by unique phonetic symbols and varied file degradation characteristics encountered in Traditional Chinese documents. [ABSTRACT FROM AUTHOR]

This paper marks the 50th anniversary of the Council on Social Work Education’s (CSWE) Minority Fellowship Program (MFP), exploring its historical development. Leveraging archival CSWE materials, it chronicles pivotal events from 1954 leading to the MFP’s inception in 1973 and its early research fellowship cohorts through 1978. This historical analysis examines the advocacy, challenges, and milestones within the CSWE and broader social movements that promoted diversity and inclusion in social work education. The MFP specifically addressed the underrepresentation of minorities by developing culturally attuned researchers and practitioners. This reflective examination enriches the social work historical record and underscores the MFP’s transformative impact in creating a profession that effectively reflects and serves our racially and ethnically diverse society. [ABSTRACT FROM AUTHOR]

Invertible colour‐to‐grey algorithms protect the colour of an image against illegal usage and distribution. These schemes hide the colour information into a grayscale version of the image, making colour recovery without proper authorization challenging. A previous study proposed the incorporation of content security by distorting the protected image to hinder illegal structure restoration. This work addresses the limitations of this method, significantly improving both processing time and the quality of the recovered colour image. Specifically, processing times are reduced from 45 h to 9 s, and the mean PSNR value is increased by up to 2.23 dB. Furthermore, this proposal achieves competitive results compared to existing approaches that do not implement content security, obtaining mean peak signal‐to‐noise ratio (PSNR) and structural similarity index measure (SSIM) values above 42.32 dB and 0.992, respectively. Finally, quantitative results are supported by visual comparisons. [ABSTRACT FROM AUTHOR]

Highlights Lower variability among replicates is associated with greater reliability for recommending irrigation timing. Eight metrics were presented for variability comparisons independent of sensor types and calibration accuracy. One neutron thermalization type tended to be less variable than six dielectric types in a comparison at 0.3 m depth. One granular matrix type tended to be less variable than one dielectric type in a comparison across the top 1 m of soil. Much of the research on irrigation scheduling sensors, especially soil water sensors, assesses and refines the accuracy of sensor calibrations. However, a sensor with an accurate calibration but high variability among replicates may require a larger-than-acceptable number of replicates for informing recommendations of optimal irrigation timing. To compare the interreplicate variability of sensors across types and calibration accuracy levels, this study presented eight metrics: (1) absolute spread-to-change ratio, (2) shifted spread-to-change ratio, (3) coefficient of change variation, (4) standard deviation of relative value, (5) standard deviation of relative change, (6) standard deviation of absolute triggering date, (7) standard deviation of shifted triggering date, and (8) standard deviation of relative triggering date. These metrics enabled comparisons either by nondimensionalizing sensor measurements or by expressing interreplicate variability in terms of time. For demonstrating their usage and their particularities, the metrics were applied to two datasets that included soil water sensor types such as neutron probe (503DR), dielectric sensor (TDR-315, CS616, CS655, HydraProbe II, 5TE, TEROS 12, Drill & Drop), and granular matrix sensor (Watermark 200SS). The neutron probe in the single-depth dataset and the granular matrix sensor in the multi-depth dataset generally displayed less interreplicate variability than other evaluated sensor types over multiple drying cycles. Future research is suggested to calculate and improve the eight metrics for identifying combinations of sensor types, deployment methods, and data interpretation techniques that minimize interreplicate variability and maximize irrigation scheduling precision. [ABSTRACT FROM AUTHOR]

High-quality datasets are essential for training high-performance models, while the process of collection, cleaning, and labeling is costly. As a result, datasets are considered valuable intellectual property. However, when security mechanisms are symmetry-breaking, creating exploitable vulnerabilities, unauthorized use or data leakage can infringe on the copyright of dataset owners. In this study, we design a method to mount clean-label dataset watermarking based on trigger optimization, aiming to protect the copyright of the dataset from infringement. We first perform iterative optimization of the trigger based on a surrogate model, with targets class samples guiding the updates. The process ensures that the optimized triggers contain robust feature representations of the watermark target class. A watermarked dataset is obtained by embedding optimized triggers into randomly selected samples from the watermark target class. If an adversary trains a model with the watermarked dataset, our watermark will manipulate the model's output. By observing the output of the suspect model on samples with triggers, it can be determined whether the model was trained on the watermarked dataset. The experimental results demonstrate that the proposed method exhibits high imperceptibility and strong robustness against pruning and fine-tuning attacks. Compared to existing methods, the proposed method significantly improves effectiveness at very low watermarking rates. [ABSTRACT FROM AUTHOR]

The rising use of 3D digital products has increased the demand for copyright protection. In this paper, we propose a novel and robust 3D watermarking method with high imperceptibility based on EMD (empirical mode decomposition) on surfaces. It first defines a normalized modulus signal on a 3D host model so as to involve EMD into 3D watermarking effectively. And then, it extracts different scale features of the defined signal by using EMD to locate the proper embedding positions. After this, the watermark signal is embedded repeatedly and cyclically into the 3D host model to enhance the robustness. The embedding strength is optimized according to a predefined fidelity parameter to control the imperceptibility of the watermark. Many experiment results show that the proposed method can obtain good results against various attacks while maintaining high invisibility, such as pseudo-random noise, Laplacian smoothing, simplification, subdivision, cropping, and similarity transformation. Furthermore, it is very competitive with the current state-of-the-art 3D watermarking techniques in terms of robustness and invisibility. [ABSTRACT FROM AUTHOR]

Water significantly influences the electrical resistivity and piezoresistive performance of piezoresistive cementitious composites (PCCs). In existing studies, it has been difficult to reflect the actual water permeability in real structures using overall moisture content of specimens. Thus, to facilitate structural health monitoring of piezoresistive cement-based sensors in aquatic service, this study evaluated cementitious composites containing multiwalled carbon nanotubes to create a piezoresistive cement-based sensor. The variations in electrical signals were monitored to assess the internal water permeability of the specimens. An improved method for the installation of laterally arranged copper electrode meshes was developed. The changes in electrical resistivity and gauge factors before and after water permeability experiment were defined as the fractional change in permeability electrical resistivity (FCPR) and the fractional change in gauge factor (FCGF), respectively. These metrics were utilized to assess the extent of water permeability in the water-permeated specimens based on the ranges of FCPR and FCGF. The experimental results indicated that (1) with an increase in water permeability time, the moisture content and seepage height of the water-permeated specimens gradually increase, the degree of decrease in electrical resistivity becomes more pronounced, and FCR has an increasing fluctuation with periodic rises and falls under the same connection; (2) the electrical signals in the semidry region above the water mark exhibit slight fluctuations, indicating that the piezoresistive cement-based sensor can provide advanced warning of water permeability; and (3) the more extensive the water permeability, the higher are the FCPR and FCGF exhibited by the piezoresistive cement-based sensors, allowing for the assessment of water permeation. This study provides a new understanding of the unique properties and potential applications of piezoresistive cement-based sensors in aquatic environments, paving the way for their future application in monitoring and maintaining aquatic services. Practical Applications: This paper introduces a piezoresistive cement-based sensor formed by incorporating carbon nanotubes into cementitious composites. However, during service of structural health monitoring in concrete structures using piezoresistive cement-based sensors. In contrast to other studies aiming to mitigate the impact of moisture, this paper leverages the high sensitivity of the piezoresistive cement-based sensor to moisture. The water permeation in the water-permeated specimens is evaluated through changes in the electrical signals. The objective is to establish the transverse and longitudinal arrangement of piezoresistive cement-based sensors arrays in RC structures in aquatic service for water permeability monitoring and damage monitoring. As the water gradually permeates the RC structure, the piezoresistive cement-based sensors at different water permeability conditions exhibit distinct electrical signal changes. This will allow for advanced warning of steel corrosion and real-time monitoring of damage development in aquatic service for RC structures. [ABSTRACT FROM AUTHOR]

To address the challenges of video copyright protection and ensure the perfect recovery of original video, we propose a dual-domain watermarking scheme for digital video, inspired by Robust Reversible Watermarking (RRW) technology used in digital images. Our approach introduces a parameter optimization strategy that incrementally adjusts scheme parameters through attack simulation fitting, allowing for adaptive tuning of experimental parameters. In this scheme, the low-frequency Polar Harmonic Transform (PHT) moment is utilized as the embedding domain for robust watermarking, enhancing stability against simulation attacks while implementing the parameter optimization strategy. Through extensive attack simulations across various digital videos, we identify the optimal low-frequency PHT moment using adaptive normalization. Subsequently, the embedding parameters for robust watermarking are adaptively adjusted to maximize robustness. To address computational efficiency and practical requirements, the unnormalized high-frequency PHT moment is selected as the embedding domain for reversible watermarking. We optimize the traditional single-stage extended transform dithering modulation (STDM) to facilitate multi-stage embedding in the dual-domain watermarking process. In practice, the video embedded with a robust watermark serves as the candidate video. This candidate video undergoes simulation according to the parameter optimization strategy to balance robustness and embedding capacity, with adaptive determination of embedding strength. The reversible watermarking is formed by combining errors and other information, utilizing recursive coding technology to ensure reversibility without attacks. Comprehensive analyses of multiple performance indicators demonstrate that our scheme exhibits strong robustness against Common Signal Processing (CSP) and Geometric Deformation (GD) attacks, outperforming other advanced video watermarking algorithms under similar conditions of invisibility, reversibility, and embedding capacity. This underscores the effectiveness and feasibility of our attack simulation fitting strategy. [ABSTRACT FROM AUTHOR]

In the realm of edge AI systems where deep learning is paramount, protecting the intellectual property (IP) of multimodal neural network models is crucial. Current watermarking solutions often bypass the intricacies of multimodal models and the unique constraints of edge environments. Addressing this, a novel watermarking scheme specifically devised for multimodal neural networks is introduced, marking a significant stride in securing these models against IP theft and unauthorized use. A discrete watermark is ingeniously embedded within each modality of a multimodal model, synthesizing a comprehensive watermark that spans the entire model. This method ensures IP protection across varied data types without hampering the model's performance or imposing undue computational demands, making it ideal for resource-limited edge devices. By leveraging the redundancies inherent in multimodal data, watermarks are embedded efficiently, maintaining model integrity and operational effectiveness. A robust verification mechanism is implemented, accurately identifying watermark presence across modalities with minimal computational overhead. Empirical validation on a benchmark dataset demonstrates the method's efficacy in embedding watermarks discreetly while preserving the model's original task performance, showing a 1 to 4% increase in watermark detection rates and a 6 to 10% reduction in false positives compared to existing approaches. This positions the scheme as an effective strategy for IP protection in multimodal neural network models, especially suited for the computational economy required in edge AI systems. The work advances neural network watermarking and addresses the urgent need for scalable IP protection solutions in the evolving AI landscape. [ABSTRACT FROM AUTHOR]

Manipulated media is becoming a prominent threat due to the recent advances in realistic image and video synthesis techniques. There have been several attempts at detecting synthetically tampered media using machine learning classifiers. However, such classifiers do not generalize well to black-box image synthesis techniques and have been shown to be vulnerable to adversarial examples. To address these challenges, we introduce FaceSigns—a deep learning-based semi-fragile watermarking technique that allows media authentication by verifying an invisible secret message embedded in the image pixels. Instead of identifying and detecting manipulated media using visual artifacts, we propose to proactively embed a semi-fragile watermark into a real image or video so that we can prove its authenticity when needed. FaceSigns is designed to be fragile to malicious manipulations or tampering while being robust to benign operations such as image/video compression, scaling, saturation, contrast adjustments, and so forth. This allows images and videos shared over the internet to retain the verifiable watermark as long as a malicious modification technique is not applied. We demonstrate that our framework can embed a 128-bit secret as an imperceptible image watermark that can be recovered with a high bit recovery accuracy at several compression levels, while being non-recoverable when unseen malicious manipulations are applied. For a set of unseen benign and malicious manipulations studied in our work, our framework can reliably detect manipulated content with an AUC score of 0.996, which is significantly higher than prior image watermarking and steganography techniques. [ABSTRACT FROM AUTHOR]

With the advances in generative adversarial networks (GAN), facial manipulations called DeepFakes have caused major security risks and raised severe societal concerns. However, the popular DeepFake passive detection is an ex-post forensics countermeasure and fails in blocking the disinformation spread in advance. Alternatively, precautions such as adding perturbations to the real data for unnatural distorted DeepFake output easily spotted by the human eyes are introduced as proactive defenses. Recent studies suggest that these existing proactive defenses can be easily bypassed by employing simple image transformation and reconstruction techniques when applied to the perturbed real data and the distorted output, respectively. The aim of this article is to propose a novel proactive DeepFake detection technique using GAN-based visible watermarking. To this front, we propose a reconstructive regularization added to the GAN's loss function that embeds a unique watermark to the assigned location of the generated fake image. Thorough experiments on multiple datasets confirm the viability of the proposed approach as a proactive defense mechanism against DeepFakes from the perspective of detection by human eyes. Thus, our proposed watermark-based GANs prevent the abuse of the pretrained GANs and smartphone apps, available via online repositories, for DeepFake creation for malicious purposes. Further, the watermarked DeepFakes can also be detected by the SOTA DeepFake detectors. This is critical for applications where automatic DeepFake detectors are used for mass audits due to the huge cost associated with human observers examining a large amount of data manually. [ABSTRACT FROM AUTHOR]

In the majority of vector map reversible watermarking algorithms, there is significant correlation between the watermarked vertices. This correlation weakens the algorithms' resistance to vertex editing attacks and limits its practical effectiveness. Therefore, this paper proposes a robust and perturbation-controlled reversible watermarking algorithm for vector maps. The algorithm is designed based on the concepts of quantized index modulation and pseudo-random mapping. Firstly, a virtual interval is created as the concealed domain of the watermark based on the precision tolerance of the data, and the tolerance is employed to restrict the range of movement of the vertices. Secondly, the proximity of the adjacent vertices inside the feature is employed as a pseudo-random seed, generating a pseudo-random integer to calculate the watermark information. This process effectively reduces the correlation among the vertices. Finally, the embedding strength is used to define multiple subintervals. The position of the vertex within these subintervals is then adjusted based on the watermark information. This process allows for the embedding of a high-capacity watermark and improves the algorithm s suitability for small-scale data. Once the watermark embedding process is finished, the average value of all vertices is computed as the key to guarantee the precision of watermark extraction. The experimental results demonstrate that the proposed watermarking algorithm exhibits strong resistance against vertex addition and deletion, feature addition and deletion, cropping, simplification, translation, scaling, and other related manipulations. Additionally, it ensures that the level of disturbance remains within a manageable range. [ABSTRACT FROM AUTHOR]

As an important infrastructure for autonomous driving, high-precision maps play a crucial role in promoting the development of autonomous driving. Their high precision and commercial value also make the security and copyright issues of high-precision maps in sharing undeniable. This paper proposes a lossless digital watermarking algorithm suitable for high-precision maps based on the order of lane storage. Firstly, by utilizing the characteristic that lane storage order transformation does not affect data accuracy, a mapping relationship between lane storage and watermark bits is established. Then, the connectivity relationships between high-precision map elements are classified to group the lanes and establish their mapping relationship with the watermark index. Finally, the storage features and connectivity of the lanes are utilized to obtain watermark bits and watermark indexes for the watermark embedding. Using OpenDRIVE map data as experimental data for algorithm verification, the results show that the algorithm can fully achieve the lossless accuracy of high-precision maps, and can resist attacks such as translation, rotation, scaling, and cropping. It has good robustness and can provide reliable technical support for copyright protection of high-precision maps. [ABSTRACT FROM AUTHOR]

Robust image watermarking that can resist camera shooting has gained considerable attention in recent years due to the need to protect sensitive printed information from being captured and reproduced without authorization. Indeed, the evolution of smartphones has made identity watermarking a feasible and convenient process. However, this process also introduces challenges like perspective distortions, which can significantly impair the effectiveness of watermark detection on freehandedly digitized images. To meet this challenge, ResNet50-based ensemble of randomized neural networks (ReRNet), a recursive convolutional neural network-based correction method, is presented for the print-cam process, specifically applied to identity images. Therefore, this paper proposes an improved Fourier watermarking method based on ReRNet to rectify perspective distortions. Experimental results validate the robustness of the enhanced scheme and demonstrate its superiority over existing methods, especially in handling perspective distortions encountered in the print-cam process. [ABSTRACT FROM AUTHOR]

In this paper, we propose an anti-counterfeit framework for electronic certificate based on QR code based watermarking and seal image based watermarking, to protect the authenticity of electronic certificate. The QR code image is encoded by the core information of an electronic certificate. The QR code based watermarking uses a binary image watermarking algorithm to embed the hash value generated by the buyer's taxpayer information into the QR code image. The seal image based watermarking embeds the seller's taxpayer information into the seal image of the certificate. In the anti-counterfeit authentication, a triple anti-counterfeit verification is proposed to confirm the authenticity of certificate information through three steps of information matching. The information on the electronic certificate is matched against the QR decoded information and the extracted watermark information to verify consistency and achieve anti-counterfeit. Experimental results show that the proposed framework can detect tampered electronic certificates well, and can effectively prevent electronic invoices from forgery. [ABSTRACT FROM AUTHOR]

To prevail over the trade-offs among the three major parameters, i.e., data transparency, robustness, and hiding capacity, is an enduring crisis in the domain of digital watermarking. In this paper, a new spatial image watermarking technique has been developed based on a model shopping pattern that could be followed at multi-stores to defy the spread-out of any contagious or airborne disease. This idea got motivated by the recent pandemic situation. Akin to maintaining distance among people on a shopping floor to avoid gathering, this proposed watermarking scheme embeds copyright information into the cover image maintaining certain distances to reduce visual distortion as well as to improve imperceptibility. Besides, unlike the conventional spatial domain-based watermarking algorithm, this proposed work tries to shift the embedding bit location in cover pixels towards the MSB, instead of LSB, to enhance robustness. The proficiency of this proposed method has been studied, and the innovation associated with this scheme has been found in improving robustness without affecting the visual quality, which has also been evaluated against some other existing relevant methodologies. [ABSTRACT FROM AUTHOR]

The paper comments on Mark Solms' Revised Standard Edition of the Complete Psychological Works of Sigmund Freud and the challenges to grasp the subtleties and ambiguity of Freud's language. It argues that "translation" is not something that just "occurs"' to a text which is already completed, but an ongoing process that carries forward and explores different layers of meaning. The author tries to show that Freud saw himself as a "translator" and that "translation" reaches at the very core of the psychoanalytical endeavour. The multiple meanings of "transference", and the translation of terms like "Nachträglichkeit" and "Zweizeitigkeit" (bi-temporality), are presented as examples of this. To conclude, Freud's dwelling in two different epistemological models, the language of neurophysiology and the language of meaning, as well as the limits of translation are discussed. [ABSTRACT FROM AUTHOR]

The rapid progress in the transfer of information and its availability are the reasons behind the widespread use of the Internet. Document images are the most complicated and challenging category among various forms of digital data to which watermarking can be applied for its security and authentication. Watermarking of the document image is a very difficult task, since these images have very limited redundancy. Because of this factor there has been very little research in this area. A comprehensive research should be carried out to ensure the effective assessment, review as well as implementation of document image watermarking techniques. This paper reviews the existing document image watermarking by considering different evaluation parameters. Based on this review, a variety of challenges and directions have been prescribed for the development of effective watermarking techniques for document images. [ABSTRACT FROM AUTHOR]

The development of new quantum watermarking is in full swing, and it is crucial to test the robustness of these proposed watermarking schemes. In this paper, different quantum circuits are introduced to realize geometric transformations on quantum images, which serve as a means of simulating geometric attacks on quantum watermark images. Receivers may get distorted images, and the extracted watermark images may also be disrupted. To recover the geometric order of the quantum watermark images, a strategy is proposed to identify the trajectory of qubit alterations. Quantum circuits can be designed accordingly to restore the original order of the watermark images, which receivers could use before the extraction process to mitigate the impact of geometric attacks. Experimental simulations demonstrate how attacks affect the quantum watermark images and the extracted watermark images. Additionally, it illustrates how quantum recovery circuits step by step act on images that have been geometrically attacked. An analysis of the complexity of geometric restoration circuits reveals that quantum circuits exhibit better performance compared to classical circuits. [ABSTRACT FROM AUTHOR]

The primary evaluation criteria for image watermarking methods encompass imperceptibility, robustness, and capacity. However, there exists a mutual dependence and constraint relationship among these three factors, making it of paramount importance to investigate how to strike a balance between them. Statistical watermarking methods that leverage the statistical characteristics of the carrier and human visual features hold promise in achieving such equilibrium among imperceptibility, robustness, and capacity. Nevertheless, statistical watermarking methods face certain issues, including poor robustness in the embedding domain and a deficiency of decoding methods, etc. In this paper, we introduce a novel approach aimed at enhancing robustness in the embedding domain by utilizing the nonsubsampled shearlet transform (NSST) domain local pseudo-Zernike moments (PZMs) magnitudes. And we designed a two-component student's-t mixture model (SMM) to represent the NSST-PZMs magnitudes, taking into account their "peak-tail" characteristics. Finally, by applying the SMM in conjunction with maximum-likelihood criteria, we derived a closed-form decoding expression. Our extensive experimental results indicate that when embedding a 1024-bit watermark sequence, our method exhibits outstanding performance, with a peak signal-to-noise ratio exceeding 58 dB and a bit error rate lower than 0.033 on the SIPI, CVG-UGR, and BOSSbase datasets. Compared to some advanced methods, our approach demonstrates significant advantages in terms of imperceptibility and robustness. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Qadisiyah Computer Science & Mathematics is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As training a high-performance deep neural network (DNN) model requires a large amount of data, powerful computing resources and expert knowledge, protecting well-trained DNN models from intellectual property (IP) infringement has raised serious concerns in recent years. Most existing methods using DNN watermarks to verify the ownership of the models after IP infringement occurs, which is reactive in the sense that they cannot prevent unauthorized users from using the model in the first place. Different from these methods, in this article, we propose an active authorization control and user's fingerprint tracking method for the IP protection of DNN models by utilizing sample-specific backdoor attack. The proposed method inversely and multiplely exploits sample-specific trigger as the key to implement authorization control for DNN model, in which the generated triggers are imperceptible and sample-specific for clean images. Specifically, a U-Net model is used to generate backdoor instances. Then, the target model is trained on the clean images and backdoor instances, which are inversely labeled as wrong classes and correct classes, respectively. Only authorized users can use the target model normally by pre-processing the clean images through the U-Net model. Moreover, the images processed by the U-Net model will contain unique fingerprint that can be extracted to verify and track the corresponding user's identity. This article is the first work that utilizes the sample-specific backdoor attack to implement active authorization control and user's fingerprint management for DNN model under black-box scenarios. Extensive experimental results on ImageNet dataset and YouTube Aligned Face dataset demonstrate that the proposed method is effective in protecting the DNN model from unauthorized usage. Specifically, the protected model has a low inference accuracy (1.00%) for unauthorized users, while maintaining a normal inference accuracy (97.67%) for authorized users. Besides, the proposed method can achieve 100% fingerprint tracking success rates on both the ImageNet and YouTube Aligned Face datasets. Moreover, it is demonstrated that the proposed method is robust against fine-tuning attack, pruning attack, pruning attack with retraining, reverse-engineering attack, adaptive attack, and JPEG compression attack. The code is available at https://github.com/nuaaaisec/SSAT. [ABSTRACT FROM AUTHOR]

Hazardous and adverse hydrological phenomena in rivers of the Russian part of the Ural River basin have been analyzed and assessed based on data of long-term observations of the level regime at hydrological gages with established critical water levels. A procedure developed in the State Hydrological Institute was used, and a generalized criterion was proposed, which enabled identifying gages with the largest flood hazard. The analysis has shown that the recurrence of hazardous hydrological phenomena at gages located at Ashcherbutak, Sergievka, Maiskii, and Adamovka settlements is >10%, with most hazardous floods observed at the Ashchebutak and Adamovka settlements. The recurrence of unfavorable hydrological phenomena at gages at the towns of Orsk and Kuvandyk and at Novoorsk Settlement is >50%. At the same gages, the mark was exceeded many times (up to four times per year) in different phases of water regime (spring flood and freshets). This indicates the need to take measures to prevent the adverse effect of water in areas subject to a high risk of inundation. Critical marks of low water level were recorded at two sites on the Ural River—the City of Orenburg and the Town of Verkhneuralsk. The lowest water level at Orenburg gage has never dropped below the level of a hazardous hydrological phenomenon. At the gage of Verkhneuralsk Town, against the background of considerable changes in the lowest water levels after 1975, its level dropped 4 times below the mark of unfavorable hydrological phenomenon. [ABSTRACT FROM AUTHOR]

In this work, a blind watermarking scheme has been proposed for medical image authentication. The scheme can embed patients' records in digital format into a digital copy of a computerized scan. In this scheme, an Integer Wavelet Transform (IWT) is applied to the medical image prior to the embedding procedure. Here LH, HH, and HL sub-bands of the IWT are used for watermark embedding. To ensure the security of the scheme a SHA-512 hash algorithm is applied to the watermark, and the hash value is embedded within the digital copy of the scanned image to ensure the authenticity of the scanned copy. Depending on a shared secret key, the IWT coefficients are selected from random positions before embedding the watermark. The results show that the proposed scheme achieved 78.5 dB PSNR with an NCC value of 0.9805, and SSIM is near about 1 after embedding 65,280 bits of watermark information. The experimental outcomes are compared with some state-of-the-art schemes to show the superiority of the proposed scheme in terms of robustness and imperceptibility. [ABSTRACT FROM AUTHOR]

The research paper introduces an advanced asymmetric biometric image encryption technique that integrates Quasi-Zernike synthesis (QZS), QR algorithm, and hybrid transforms for enhanced security and integrity. The encryption process incorporates QR decomposition, generating an orthogonal matrix Q that adds obfuscation and randomness to the image representation. QR is also resilient to attacks that exploit vulnerabilities in PTFT-based cryptosystems. QZS further improves security by capturing spatial information and complex features. The use of the compressed sparse row (CSR) format enables efficient storage and secure transmission. This integration provides a robust solution for confidential, secure image communication, applicable in military, surveillance, and sensitive data transfer scenarios. Moreover, watermarking ensures protection from unauthorized access, strengthening security measures. Experimental evaluations demonstrate the proposed algorithm's robustness against common attacks, including noise addition, compression, and filtering. The results showcase high-quality image reconstruction, negligible information loss, affirming the scheme's effectiveness in achieving secure image encryption. [ABSTRACT FROM AUTHOR]

In recent years, reversible watermarking has drawn significant interest from the research community. Watermarking engineering is the practice of embedding a piece of hiding information into a digital medium for several applications including Copyright Protection, Ownership Assertion, and Data Authentication. Reversible watermarking techniques possess the ability to regenerate the embedded message in its entire and without any slightest degradation at the decoding side. The research work presented a novel reversible watermarking technique based on Depth Image Based Rendering (DIBR) for three-dimensional television (3DTV). The proposed technique is inspired by the idea of Difference Expansion (DE). Instead of computing and expanding dv in the same image, the disparity, or the shift error, between the stereoscopic left and right images is used along with the payload as a watermark, which is subsequently embedded in the depthmap of the image. The advantage of doing so is that the depthmap alone can be used in computing the stereoscopic images again. The outcome of the experimental results demonstrates that the proposed technique is reversible with high embedding capacity and imperceptibility. This novel contribution promises ownership protection through the watermark and can generate anaglyph images without the corresponding left and right images. It is also easy to implement in 3DTV DIBR systems for digital display and recording. [ABSTRACT FROM AUTHOR]

Due to the advancement in biomedical technologies, to diagnose problems in people, a number of psychological signals are extracted from patients. We should be able to ensure that psychological signals are not altered by adversaries and it should be possible to relate a patient to his/her corresponding psychological signal. As far as our awareness extends, none of the existing methods possess the capability to both identify and verify the authenticity of the ECG signals. Consequently, this paper introduces an innovative dual-layer data-embedding approach for electrocardiogram (ECG) signals, aiming to achieve both signal identification and authenticity verification. Since file name-based signal identification is vulnerable to modifications, we propose a robust watermarking method which will embed patient-related details such as patient identification number, into the medically less-significant portion of the ECG signals. The proposed robust watermarking algorithm adds data into ECG signals such that the patient information hidden in an ECG signal can resist the filtering attack (such as high-pass filtering) and noise addition. This is achieved via the use of error buffers in the embedding algorithm. Further, modification-sensitive fragile watermarks are added to ECG signals. By extracting and checking the fragile watermark bits, we can determine whether an ECG signal is modified or not. To ensure the security of the proposed mechanism, two secret keys are used. Our evaluation demonstrates the usefulness of the proposed system. [ABSTRACT FROM AUTHOR]

Addressing the compatibility challenges surrounding the robustness and reversibility of existing video watermarking techniques, this study introduces a novel video dual-domain blind watermarking algorithm leveraging the Hadamard transform. Specifically tailored for H.264 video copyright protection, the algorithm initially organizes video frames and identifies key frames for watermark embedding. Prior to embedding, the robust watermark undergoes coding preprocessing to optimize its integration. Subsequently, a 4 × 4 block is expanded based on the selected embedding position within the frame, followed by the application of the Hadamard transform to the enlarged block. The 1-bit robust watermark information is then embedded via the coefficient pair located in the first row of the Hadamard coefficient matrix corresponding to the expanded block. Additionally, a reversible watermark, designed to mitigate the distortions introduced during robust embedding, is generated and embedded into the remaining coefficients of the coefficient matrix using reversible embedding techniques. During watermark extraction, the dual-domain watermark can be retrieved exclusively through reversible extraction methodologies by analyzing the size relationship of coefficient pairs, eliminating the need for access to the original video data. To bolster the algorithm's robustness, a majority-subordinate voting system is devised and implemented, effectively enhancing its resilience. Experimental findings demonstrate that, compared to similar approaches, this algorithm not only enhances the reversibility of video restoration but also exhibits superior robustness and meets the requirements for imperceptibility. [ABSTRACT FROM AUTHOR]

Most of the existing research on video watermarking schemes focus on improving the robustness of watermarking. However, in application scenarios such as judicial forensics and telemedicine, the distortion caused by watermark embedding on the original video is unacceptable. To solve this problem, this paper proposes a robust reversible watermarking (RRW)scheme based on multi-layer embedding in the video compression domain. Firstly, the watermarking data are divided into several sub-secrets by using Shamir's (t, n)-threshold secret sharing. After that, the chroma sub-block with more complex texture information is filtered out in the I-frame of each group of pictures (GOP), and the sub-secret is embedded in that frame by modifying the discrete cosine transform (DCT) coefficients within the sub-block. Finally, the auxiliary information required to recover the coefficients is embedded into the motion vector of the P-frame of each GOP by a reversible steganography algorithm. In the absence of an attack, the receiver can recover the DCT coefficients by extracting the auxiliary information in the vectors, ultimately recovering the video correctly. The watermarking scheme demonstrates strong robustness even when it suffers from malicious attacks such as recompression attacks and requantization attacks. The experimental results demonstrate that the watermarking scheme proposed in this paper exhibits reversibility and high visual quality. Moreover, the scheme surpasses other comparable methods in the robustness test session. [ABSTRACT FROM AUTHOR]

In recent years, the advancement of digital image processing technology and the proliferation of image editing software have reduced the technical barriers to digital image processing, enabling individuals without professional training to modify and edit images at their discretion. Consequently, the integrity and authenticity of the original image content assume greater significance. The current techniques for detecting tampering in watermark embedding are inadequate in terms of security, efficiency, and image restoration quality. In light of the aforementioned considerations, this paper puts forth an algorithm for the detection and restoration of tampered images, which employs a chaotic watermark embedding technique. The algorithm employs a chaotic system to establish a mapping relationship between image sub-blocks, thereby ensuring the randomness of the watermark information with respect to the positioning of the original image block and enhancing the security of the algorithm. Furthermore, the detection algorithm utilizes layered tampering detection to enhance the overall accuracy of the detection process and facilitate the extraction of the fundamental information required for image restoration. The restoration algorithm partially designs a weight assignment function to distinguish between the original image block and the main restored image block, thereby enhancing restoration efficiency and quality. The experimental results demonstrate that the proposed algorithm exhibits superior tamper detection accuracy compared to traditional algorithms, and the quality of the restored images is also enhanced under various simulated tamper attacks. [ABSTRACT FROM AUTHOR]