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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]

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]

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 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]

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 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]

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]

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]

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]

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]

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]

The crazy, unconscious use of the Internet, and the increase in cybercrime and hacking, which resulted in the loss of a large number of sensitive data, the risk of piracy, etc. were the motivation for protecting rights, securing and hiding essential information such as images, audio, etc. Watermarking was a popular security, copyright and privacy approach and one of the problems in modern quantum digital computing that produced more powerful quantum protection techniques than the classical one that used classical techniques such as digital wavelet transformation and Hungarian algorithms. Recent advances in quantum image representation and processing help to manipulate data hidden. This paper proposes a new watermark technique in which a color watermark image is hidden within another color-carrying image after transformation into the quantum domain using the recently completed quantum color image representation technique. Finally, a comparison is made between the proposed method and the most recent watermarking techniques is establish in terms of PSNR (peak signal-to-noise ratio), which shows the high precision of the proposed technique and improves the imperceptibility compared to the related schemes. [ABSTRACT FROM AUTHOR]

Arabic text watermarking is the process of hiding private data for secure authentication and copyright protection. This research proposes different emerging watermarking approaches to hide secrecy using adjusted diacritical Arabic text hiding methodology. The study examines and compares two innovative watermarking schemes on the same platform testing its implementations utilizing Imam Nawawi's forty hadith text standard benchmark, remarking attractive contribution. The exploration remarked a significant improvement aiming to deal with its Arabic letters and diacritics, as for practical watermarking secrecy applications. The proposed hiding system models are investigated in terms of performance metrics of capacity, security, and robustness. The works are further compared to other state-of-the-art relative approaches showing very interesting remarks nominating one of our proposals as best applicable selection for 35% of the time and the second with different error rate percentages not remarking the best in many cases, but noted 7.5% as second rank, 10% as third assortment, and 17.5% as fourth preference, running the investigations fairly for all testing bench mark database analysis. [ABSTRACT FROM AUTHOR]

While video is a vital component of modern communication, it also presents opportunities for manipulation and unauthorized access. The preservation and authenticity of video footage have become increasingly important as digital video data expands. Video authentication techniques ensure video integrity, confidentiality, availability, data sharing, and privacy during the data lifecycle. A video authentication technique that can prevent the copying, tampering, or alteration of video footage without permission is digital watermarking. The suggested method is notable for its ease of use, efficacy, and efficiency in handling the video authentication problem. Whereas current methods could use intricate algorithms or need much processing power, the suggested approach stands out by directly utilizing hash-based watermarking that employs SHA256 hashing and authentication. It is appropriate for realtime applications as it offers strong security against tampering while preserving computing efficiency by embedding the hash value of each odd-numbered frame into the subsequent evennumbered frame. The proposed approach is a secure and economical option for video authentication, improving the security and authenticity of video content for use in various applications. [ABSTRACT FROM AUTHOR]

This article examines the concept of "joining" in the works of Margaret Cavendish, Duchess of Newcastle. Cavendish believed in combining her texts into bound volumes, creating composite collections that required readers to engage with different disciplines and genres. The article discusses the presence of these bound collections, known as Sammelbände, in various European libraries, including the Antwerp Public Library, the Bibliothèque Mazarine in Paris, and the Cambridge University Library. The author suggests that further study of these collections would be valuable. The article also explores Cavendish's philosophy of joining and its relationship to her atomic philosophy, highlighting the importance of interdisciplinary reading practices. [Extracted from the article]

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Recent research advances in implicit neural representation have shown that a wide range of video data distributions are achieved by sharing model weights for Neural Representation for Videos (NeRV). While explicit methods exist for accurately embedding ownership or copyright information in video data, the nascent NeRV framework has yet to address this issue comprehensively. In response, this paper introduces MarkINeRV, a scheme designed to embed watermarking information into video frames using an invertible neural network watermarking approach to protect the copyright of NeRV, which models the embedding and extraction of watermarks as a pair of inverse processes of a reversible network and employs the same network to achieve embedding and extraction of watermarks. It is just that the information flow is in the opposite direction. Additionally, a video frame quality enhancement module is incorporated to mitigate watermarking information losses in the rendering process and the possibility of malicious attacks during transmission, ensuring the accurate extraction of watermarking information through the invertible network's inverse process. This paper evaluates the accuracy, robustness, and invisibility of MarkINeRV through multiple video datasets. The results demonstrate its efficacy in extracting watermarking information for copyright protection of NeRV. MarkINeRV represents a pioneering investigation into copyright issues surrounding NeRV. [ABSTRACT FROM AUTHOR]

As neural radiance fields continue to advance in 3D content representation, the copyright issues surrounding 3D models oriented towards implicit representation become increasingly pressing. In response to this challenge, this paper treats the embedding and extraction of neural radiance field watermarks as inverse problems of image transformations and proposes a scheme for protecting neural radiance field copyrights using invertible neural network watermarking. Leveraging 2D image watermarking technology for 3D scene protection, the scheme embeds watermarks within the training images of neural radiance fields through the forward process in invertible neural networks and extracts them from images rendered by neural radiance fields through the reverse process, thereby ensuring copyright protection for both the neural radiance fields and associated 3D scenes. However, challenges such as information loss during rendering processes and deliberate tampering necessitate the design of an image quality enhancement module to increase the scheme's robustness. This module restores distorted images through neural network processing before watermark extraction. Additionally, embedding watermarks in each training image enables watermark information extraction from multiple viewpoints. Our proposed watermarking method achieves a PSNR (Peak Signal-to-Noise Ratio) value exceeding 37 dB for images containing watermarks and 22 dB for recovered watermarked images, as evaluated on the Lego, Hotdog, and Chair datasets, respectively. These results demonstrate the efficacy of our scheme in enhancing copyright protection. [ABSTRACT FROM AUTHOR]

A reversible image authentication method based on wavelet-domain prediction error histogram translation is proposed. First, the image to be authenticated performs a Haar wavelet transform. Second, the low-frequency and high-frequency subbands obtained from the transform are separately partitioned into blocks, and an authentication code is generated based on the coefficient values and locations of these blocks. Then, the authentication code is embedded into the blocks using the improved pixel value ordering (IPVO) method, and the image containing the authentication information is reconstructed using the Haar wavelet inverse transform. In the authentication stage, the tampering detection function is realized by extracting the authentication code and generating the authentication code. If the image is not tampered with, the original image can be recovered losslessly. Experimental results show that the method provides more accurate detection results and ensures a certain level of image quality. [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]

Image steganography is the art of hiding information in a cover image in such a way that a third party does not notice the hidden information. This paper presents a novel technique for image steganography in the spatial domain. The new method hides and recovers hidden information of substantial length within digital imagery while maintaining the size and quality of the original image. The image gradient is used to generate a saliency image, which represents the energy of each pixel in the image. Pixels with higher energy are more salient and they are valuable for hiding data since their visual impairment is low. From the saliency image, a cumulative maximum energy matrix is created; this matrix is used to generate horizontal seams that pass over the maximum energy path. By embedding the secret bits of information along the seams, a stego-image is created which contains the hidden message. In the stego-image, we ensure that the hidden data is invisible, with very small perceived image quality degradation. The same algorithms are used to reconstruct the hidden message from the stego-image. Experiments have been conducted using two types of images and two types of hidden data to evaluate the proposed technique. The experimental results show that the proposed algorithm has a high capacity and good invisibility, with a Peak Signal-to-Noise Ratio (PSNR) of about 70, and a Structural SIMilarity index (SSIM) of about 1. [ABSTRACT FROM AUTHOR]

The real threat to the privacy of a plain document exchanged over insecure channels is content manipulation or eavesdropping by unauthorized parties. To protect a transferred document, the architecture proposed in this paper offers encryption, authentication, and scrambling GAN with shuffle confusion (EAGAN) comprehensively with a relatively rapid execution time average of 2s for each colored document. The EAGAN encrypts and signs the document content at the origin point (sender side) and then decrypts and verifies the cipher document at the receiver side, achieving two verification levels and three levels of confidentiality, reinforced with two keys of chaos and equal document content. Each document has a unique hash value (signature or identity) with a QR code watermark to detect forgery, even when the change is small (even by one bit), without depending on any third party. If case the intermediate encrypted document, the neural network model (Decoder-Key), the cipher document, or the initial values of the Chaos Key are leaked, an unauthorized person won't be able to retrieve the document. As proven by the experiments in this paper, EAGAN has characteristics that make it more resistant to security threats. [ABSTRACT FROM AUTHOR]

Federated learning (FL) enables users to train the global model cooperatively without exposing their private data across the engaged parties, which is widely used in privacy-sensitive business. However, during the life cycle of FL models, both adversaries' attacks and ownership generalization threaten the FL models' copyright and affect the models' reliability. To address these problems, existing model watermarking techniques can be used to verify FL model's ownership. However, due to the lack of credible binding from "model extracted watermarks" to "ownership verification", it is difficult to form a closed-loop watermarking framework for copyright protection. Therefore, starting from the shortcomings of the current watermark verification scheme, this article proposed WFB, a blockchain-empowered watermarking framework for ownership verification of federated models. Firstly, we propose a improved watermark generation algorithm to solve the credibility issue of watermarks. Secondly, we propose a watermark embedding method in federated learning, while blockchain technology is used to ensure the credible storage of watermark information throughout the process. Thirdly, the credibility of ownership verification is improved because of the watermark authenticity. Experimental results demonstrate the fidelity, effectiveness and robustness of WFB, with other superiorities such as improving process security and traceability. [ABSTRACT FROM AUTHOR]

Having in mind applications in statistics and machine learning such as individualized care monitoring, or watermark detection in large language models, we consider the following general setting: When monitoring a sequence of observations, Xt$$ {X}_t $$, there may be additional information, Zt$$ {Z}_t $$, on the environment which should be used to design the monitoring procedure. This additional information can be incorporated by applying threshold functions c(Zt)$$ c\left({Z}_t\right) $$ to the standardized measurements to adapt the detector to the environment. For the case of categorical data encoding of discrete‐valued environmental information we study several classes of level α$$ \alpha $$ threshold functions including a proportional one which favors rare events among imbalanced classes. For the latter rule asymptotic theory is developed for independent and identically distributed and dependent learning samples including data from new discrete autoregressive moving average model (NDARMA) series and Hidden Markov Models. Further, we propose two‐stage designs which allow to distribute in a controlled way the α$$ \alpha $$ budget over an a priori partition of the sample space of Zt$$ {Z}_t $$. The approach is illustrated by a real medical data set. [ABSTRACT FROM AUTHOR]

Telemedicine is the field that uses medical images for diagnosing various diseases. The transmitting and storing of medical images via the cloud-based network must meet several stringent criteria, including confidentiality, validity, and security, and are viewed as very sensitive, irrespective of the image processing context. Medical image copyright protection has been essential since little alterations can even put the lives of patients in danger. Hence numerous significant watermarking methods need to be developed. Watermarking conceals sensitive data by embedding it in a harmless medium, such as a cover. The challenging issue of quick and extremely safe image encryption can be solved more effectively with chaos-based cryptography. Thus the image is encrypted by using enhanced chaos with the Fruit Fly Optimization Algorithm (FFOA). The work proposes an efficient medical image watermarking approach using the Two-level Stationary Wavelet Transform (SWT) and Singular Value Decomposition (SVD) technique on chaotic encrypted medical images. Finally, the proposed method is compared with existing methods to demonstrate higher performance. To check robustness and imperceptibility results, the work is examined under various attacks and produces good results in Peak Signal-to-Noise Ratio (PSNR) and Normalized Cross Correlation (NCC) measures. For different kinds of medical images, the PSNR of the suggested methodology is greater than 40 dB, and NCC values are close to 1 illustrating the technique's superior efficacy. [ABSTRACT FROM AUTHOR]

To solve the copyright problem of audio data, many singular value decomposition (SVD)-based audio watermarking schemes have been proposed, however, most SVD-based schemes cannot improve the imperceptibility and robustness while guaranteeing a certain embedding capacity. Therefore, we propose a new SVD-based adaptive robust audio watermarking method. In this method, after framing the host audio signal, a discrete wavelet transform (DWT) is performed on each frame, and then the obtained DWT coefficients are divided into two segments using a sub-sampling operation, and the SVD is performed on these two segments and the mean value of the two singular values is calculated. Then the watermark bits are embedded by modifying the singular values of the two segments using differential embedding method. In the above watermark embedding process, the proposed adaptive method generates different sizes of embedding parameters according to the original signal features of each frame to minimize the degradation of perceived quality. During the watermark extraction process, the watermark can still be correctly extracted without the original audio signal and embedding parameters. The experimental results show that the scheme is more robust than existing audio watermarking schemes under various attacks with a certain embedding capacity guaranteed. [ABSTRACT FROM AUTHOR]

Ocean stratification on Arctic shelves critically influences nutrient availability, essential for primary production. However, discerning the changes in stratification and their drivers is challenging. Through the use of a highresolution ocean-sea-ice model, this study investigates the variability in stratification within the northeastern Chukchi Sea over the period from 1987 to 2016. Our analysis, validated against available observations, reveals that summers with weak stratification are marked by a warmer water column that features a saltier upper layer and a fresher lower layer, thereby diminishing the vertical density gradient. In contrast, summers with strong stratification are characterized by a cooler column with a fresher upper layer and a saltier lower layer, resulting in an increased density gradient. This variability is primarily driven by the timing of sea-ice retreat and the consequent variations in meltwater flux, with early retreat leading to less meltwater and saltier surface conditions. This factor significantly outweighs the influence of changes in circulation and associated lateral freshwater transport driven by the Bering Strait inflow. We also find that the synchronization of sea-ice retreat and Bering Strait inflow intensity is linked to the timing and strength of the Aleutian Low's westward shift from the Gulf of Alaska to the Aleutian Basin in the early winter. These insights are crucial for understanding nutrient dynamics and primary production in the region. Furthermore, monitoring sea-ice retreat timing could serve as a useful proxy for predicting subsequent summer stratification changes. [ABSTRACT FROM AUTHOR]

Music infringement has emerged as a significant concern and the copyright protection of digital music is becoming more important. Most music copyright protection methods use images as watermark information. Hence, the original watermark needs to be embedded in the music multi-times to achieve copyright protection. It cannot guarantee the accuracy of the embedded watermark. And it may also degrade the quality of the watermarked music. In order to solve that issue, a blind music watermarking is proposed to protect the copyright of music. Comparing with the existing music copyright protection methods, the proposed method generates a watermark from the speech of a copyright owner that can ensure the uniqueness of the watermark. When generating the watermark, the multi-band power distribution of speech recording is calculated first. Subsequently, the discrete cosine transformation is applied to the sub-graphs of the multi-band power distribution, and the watermark is generated using the resulting Discrete Cosine Transformation (DCT) coefficients. After that, the watermark is embedded into the coefficients of Discrete Wavelet Transformation (DWT) using quantization norms. The experiments demonstrate that the Signal to Noise Ratio (SNR) values are higher than 58db between the original music and watermarked music. Meanwhile, the Bit Error Rate (BER) and Normalized Correlation (NC) values are zeros and ones, respectively. In conclusion, the evaluation results show that the proposed method can achieve excellent imperceptibility and robustness. Additionally, it can also effectively resist conventional processing operations such as noise addition, filtering, and so on. [ABSTRACT FROM AUTHOR]

There are various challenges facing quantum watermarking, of which the most significant one is that unlike classical watermarking, there remain few achievements made to build a complete system. Allowing for this, a two-domain quantum color image watermarking scheme based on least significant bit (LSB) algorithm is proposed in this paper. For the purpose of image representation, the image of the carrier and that of the watermark are represented through the novel quantum representation of color digital images (NCQI) (Sang et al. in Quantum Inf Process 16:1–14, 2017), the size of which is 2 n × 2 n and 2 n - 1 × 2 n - 1 , respectively. Firstly, the watermark image is scrambled by conducting operation on the color and position of the image. Through this simultaneous scrambling of color and position, the privacy of watermark images is better protected. Secondly, the scrambled watermark image is embedded into the spatial domain of the carrier image using the LSB algorithm. Additionally, the scrambled watermark image is embedded into the frequency domain of the carrier image through quantum Fourier transform and LSB algorithm, thus improving the capability to withstand illegal attacks. Lastly, both simulation experiment and comparative analysis are conducted to demonstrate the satisfactory visual quality of the proposed scheme and its strong robustness to the salt and pepper noises. These positive findings contributed to enriching the existing theory of color quantum image watermarking. [ABSTRACT FROM AUTHOR]

Henry Purcell's opera, Dido and Aeneas, has a complex history due to a lack of contemporary sources. Robert Shay's new edition of the opera offers a clear and comprehensive understanding of its origins and musical score. Shay examines the earliest documents related to the opera's premiere and explores its revivals from 1700 to 1787. He also analyzes the three primary music manuscripts and makes editorial choices based on their historical significance. Shay's edition provides a clean and accurate representation of the surviving music of Dido and Aeneas. [Extracted from the article]

Unconditional security for smart grids is defined. Cryptanalyses of the watermarked security of smart grids indicate that watermarking cannot guarantee unconditional security unless the communication within the grid system is unconditionally secure. The successful attack against the dynamically watermarked smart grid remains valid even with the presence of internal noise from the grid. An open question arises if unconditionally authenticated secure communications within the grid, together with tamper resistance of the critical elements, are satisfactory conditions to provide unconditional security for the grid operation. [ABSTRACT FROM AUTHOR]

In the digital age, securing multimedia content against unauthorized use is critical. Traditional watermarking techniques often struggle with robustness against various attacks. This study introduces a novel DeepMarkNet approach for robust image and video watermarking. DeepMarkNet leverages deep learning to embed and detect watermarks with high resilience to common distortions. The method employs a Convolutional Neural Network (CNN) for embedding and a dualstream architecture for detection. Experimental results demonstrate DeepMarkNet effectiveness, achieving a 98.5% detection accuracy and maintaining watermark integrity under compression and noise attacks. This outperforms conventional techniques by 15% in robustness. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications 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.)

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications 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.)

This paper attempts to demonstrate that bottom-up popular vote processes (the optional referendum and citizens' initiative) focused on ordinary legislation can help to improve democratic self-government by uniquely facilitating the development and expression of various forms of political agency. Most significantly, it is argued that such popular vote processes can be designed in ways that endow them with significant deliberative credentials. Methodologically, the paper employs Mark E. Warren's problem-based approach to democratic theory, which provides the conceptual tools necessary to advance a) a novel case for recovering popular vote processes as a valid institutional toolkit for exploration by democratic theory and b) an empirically informed set of best practices that can provide critical resources for normatively scrutinizing existing institutionalizations of bottom-up popular vote processes across the world. [ABSTRACT FROM AUTHOR]

Advanced internet technology allows unauthorized individuals to modify and distribute digital images. Image watermarking is a popular solution for copyright protection and ensuring digital security. This research presents an embedding scheme with a set of conditions using non-subsampled Shearlet transform (NSST), lifting wavelet transform (LWT), and discrete cosine transform (DCT). Red and green channels are employed for the embedding process. The red channel is converted by NSST-LWT. The low-frequency area (LL) frequency is then split into small blocks of 8×8, each partition block is then transformed by DCT. The DCT coefficient of (3,4), (5,2), (5,3), (3,5), called matrix M1, and (2,5), (4,3), (6,2), (4,4), called matrix M2 are selected for singular value decomposition (SVD) process. With a set of conditions, the watermark bits are incorporated into those singular values. The green channel is cropped to get the center image before splitting into 4×4 pixels. The block components are then selected based on the least entropy value for the embedding regions. The selected blocks are then computed using LWT-SVD. A set of conditions for U(1,1) and U(2,1) are used to incorporate the watermark logo. The experimental findings reveal that the suggested scheme achieves high imperceptibility and resilience under various evaluating attacks with an average peak signal-to-noise ratio (PSNR) and correlation value (NC) values are up to 43.89 dB and 0.96, respectively. [ABSTRACT FROM AUTHOR]