Embedding Position Research Articles

Polyamide membranes with tannic acid-ZIF-8 for highly permeable and selective ion-ion separation

Highly permeable polyamide (PA) membranes with precise ion selection can be used for many energy-efficient chemical separations but are limited by membrane inefficiencies. Herein, polyphenol-mediated ZIF-8 nanoparticles with hydroxyl-rich hollow structure were synthesized by tannic acid tailored regulation. PA-based membranes with fast penetration, high retention, and precise Cl−/SO42− selection were then synthesized through spatially and temporally controlling interfacial polymerization with modified ZIF-8 nanoparticles (tZIF-8) as aqueous phase additives or as interlayers. The effects of the embedding position of tZIF-8 on the structure, morphology, physicochemical properties, and performance of PA-based membranes were explored through a sequence of characterization techniques. The results revealed that the PA-based membrane with tZIF-8 embedded in the PA layer could achieve a high water permeance of 24.8 L m−2 h−1 bar−1 with a high retention of 99.4 % Na2SO4 and a Cl−/SO42− selectivity of 141, which was superior to most state-of-the-art PA-based membranes. Comparatively, the Cl−/SO42− selection of the PA-based membrane with tZIF-8 embedded between the PA layer and the substrate was 136, while the water permeance was slightly enhanced to 28.2 L m−2 h−1 bar−1. Excitingly, the resulting membranes all exhibit superior antifouling properties and stability. Our facile strategy for tuning membrane microstructures provides new ideals into the development of highly permeable and excellently selective PA-based membranes for precise ion sieving.

Errata to “Analysis of Magnetomechanical Properties at the Tooth-Yoke Embedding Position in Grain-Oriented Electrical Steel Electric Motors”

Errata to “Analysis of Magnetomechanical Properties at the Tooth-Yoke Embedding Position in Grain-Oriented Electrical Steel Electric Motors”

A Stealthy Communication Model for Protecting Aggregated Results Integrity in Federated Learning

Given how quickly artificial intelligence technology is developing, federated learning (FL) has emerged to enable effective model training while protecting data privacy. However, when using homomorphic encryption (HE) techniques for privacy protection, FL faces challenges related to the integrity of HE ciphertexts. In the HE-based privacy-preserving FL framework, the public disclosure of the public key and the homomorphic additive property of the HE algorithm pose serious threats to the integrity of the ciphertext of FL’s aggregated results. For the first time, this paper employs covert communication by embedding the hash value of the aggregated result ciphertext received by the client into the ciphertext of local model parameters using the lossless homomorphic additive property of the Paillier algorithm. When the server receives the ciphertext of the local model parameters, it can extract and verify the hash value to determine whether the ciphertext of the FL’s aggregated results has been tampered with. We also used chaotic sequences to select the embedding positions, further enhancing the concealment of the scheme. The experimental findings demonstrate that the suggested plan passed the Welch’s t-test, the K–L divergence test, and the K–S test. These findings confirm that ciphertexts containing covert information are statistically indistinguishable from normal ciphertexts, thereby affirming the proposed scheme’s effectiveness in safeguarding the integrity of the FL’s aggregated ciphertext results. The channel capacity of this scheme can reach up to 512 bits per round, which is higher compared to other FL-based covert channels.

A Video Dual-Domain Blind Watermarking Algorithm Based on Hadamard Transform

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.

A secure image steganography based on LSB technique and 2D chaotic maps

This paper introduces a pioneering, effective data-hiding algorithm using different sizes of cover images and different types of secret data (image, txt, and audio). The proposed algorithm is based on a hybrid 1D chaotic map combined with a 2D composite chaotic map paired with optimization and Least Significant Bits (LSB) techniques. We employ chaotic maps to create the initial embedding position vector, and LSB to hide the secret message in the cover image. The optimization technique utilizes the chaotic maps to optimize the random embedding sites, looking for value matches between the cover image pixels and the secret data bits. This partial improvement leads to a high-quality stego-image, a large embedding capacity, imperceptibility to the human eye, undetectability, and enhanced security. The outcomes of the extensive experiments show that when the payload is 4.2 bits per pixel, the peak signal-to-noise ratio value (PSNR), which expresses the visual quality, is above 30 dB. Furthermore, the experimental results illustrate that the system exhibits robustness against various types of attacks. As a final point of strength, the large key space (2^206) prevents the secret data from being resolved if stego-images are accessed by intruders or third parties. Even minor changes in key bits can significantly alter the extracted secret data, resulting in entirely different secret data.

A DNN robust video watermarking method in dual-tree complex wavelet transform domain

Deep learning is increasingly being applied in the field of robust watermarking. However, the existing deep learning-based video watermarking methods only uses spatial domain information as the input and the robustness against attacks such as H.264/AVC compression is still not strong. Therefore, this paper proposes a deep learning-based robust video watermarking method in dual-tree complex wavelet transform (DT-CWT) domain. The video frames are transformed into the DT-CWT domain and the suitable high-pass subbands are selected as candidate embedding positions. Then, the 2D and 3D convolutions are combined to extract both intra-frame spatial features and inter-frame temporal features for finding the stable and imperceptible coefficients for watermark embedding in the candidate positions. The convolutional attention module (CBAM) is used to further adjust the embedding coefficients and strengths. In addition, the attack layer, where a differentiable proxy is specially designed in this paper for the simulation of non-differentiable H.264/AVC compression, is introduced to generate distorted watermarked videos for improving the robustness against different attacks. Experimental results show that our method is superior to both the existing deep learning-based methods and traditional methods in the robustness against both spatial and temporal attacks while preserving high video quality. The source code is available at https://github.com/imagecbj/A-DNN-Robust-Video-Watermarking-Method-in-DT-CWT-Domain.

Enhanced prediction of reflected spectrum for FBG sensors with metallic coating embedded in CFRP composites: Unveiling the impact of process-induced residual stress and coating thickness

The occurrence of peak-split or distortion in the reflected light of fiber Bragg grating (FBG) sensors with metallic coatings embedded in composites is inevitable during the curing process, regardless of protection layers. In this study, we present a comprehensive methodology to numerically predict the reflected spectrum of metallic-coated FBG sensors, considering the process-induced residual stress in carbon fiber/epoxy composites. The finite element analysis was utilized to simulate the residual stress, which primarily arises from mechanical, thermal, and chemical cure mechanisms of the composites, including the thermosetting resin. Subsequently, the reflected spectra were calculated using the coupled mode theory. Contrary to common expectations, our findings indicate that the coating thickness has minimal influence on the reflected spectrum, while the residual stress and embedding position significantly impact it. By employing this proposed methodology, the number of experimental trials can be reduced, enabling the development of robust structural and state monitoring systems for composites using metallic-coated FBG sensors.

Effects of non-subsidized industrial policies on embedding position of power lithium-ion battery manufacturers in global value chain: Firm level evidence from China

Given the phase-out of subsidies, the Chinese government has issued a series of non-subsidized industrial policies to support the development of the power Lithium-ion Battery (PLiB) industry, recognized as pivotal for achieving carbon neutrality in the transportation department. However, existing literature provides limited evidence on the contribution of non-subsidized industrial policies, particularly to the PLiB industry. To bridge this gap, using data of 31 PLiB listed firms and 279 relevant non-subsidized industrial policies, this paper analyzes the effects of non-subsidized industrial policies on the global value chain (GVC) embedding position. The results reveal that non-subsidized industrial policies significantly raise the GVC embedding position of Chinese PLiB firms, and the positive effects are prone to increase over the research period. Further investigation shows that there is heterogeneity among PLiB firms, with the raising effects on the GVC embedding position being more significant in eastern and middle PLiB firms, private PLiB firms and midstream PLiB firms. The mechanism verification shows that non-subsidized industrial policies raise GVC embedding position of PLiB firms through technological innovation effect and scale economy effect, while competitiveness effect does not give full play. These findings provide important recommendations for better design of the industrial policies to improve the international competitiveness of PLiB industry and other strategic emerging industries.

Tunable deformation behavior of 4D printed shape memory polymers with embedded pre-stretched elastomers

Four-dimensional (4D) printing has attracted considerable attention for its diverse capabilities. However, there has been limited discussion regarding the direct programming of a 3D shape during the 4D printing process to enable active deformation under external stimuli. Therefore, we have developed a novel 4D printing method that imparts active deformation capabilities to shape memory polymers (SMPs) by embedding pre-stretched elastomers using UV-curing bonding. Comprehensive studies have been conducted on elastomer modifications, the bonding performance between pre-stretched elastomers with SMPs, as well as the thermally and electrically responsive deformation behaviors of active deformation SMPs samples. The bending curvature of thermally responsive active deformation SMPs samples exhibited a clear linear relationship with the stretch ratio and the offset position of the pre-stretched elastomers. By adjusting the inclination angle and embedding position of pre-stretched elastomers, the SMPs samples successfully achieved various forms of active deformation, including torsional, segmented, and wave-shaped deformations. Moreover, the integration of an embedded silver nanowire (AgNW) electrothermal layer (ETL) in SMPs samples facilitated electrically responsive deformations. The deformation speed and force of SMPs samples can be regulated by controlling the applied current. Thermally responsive flowers, electrically responsive cross-shaped grippers, as well as reverse deformation grippers were designed and printed. These application examples effectively exemplified the immense potential of active deformation SMPs in the fields of soft robotics, flexible electronic devices, and aerospace equipment.

Improved Convolutional Neural Network Algorithm for Student Behavior Detection in the Classroom

The performance of the existing student classroom behavior detection model is affected by various aspects such as dataset, algorithm and height as well as the differences between different classrooms, and there are problems such as a single dataset, low accuracy and low efficiency. In order to improve the accuracy of student classroom behavior detection algorithm, this paper proposes a student classroom behavior detection method based on improved convolutional neural network algorithm. Firstly, the student behavior detection dataset is constructed, and the student classroom behavior detection technology scheme is designed; secondly, in order to improve the detection accuracy, the features are extracted by using the new jumping bi-directional paths, and the attention mechanism module is added at different positions to improve the path aggregation network; weekly, the embedding positions of the attention mechanism strategy are determined by analyzing multiple sets of experiments, and the proposed student classroom behavior detection algorithm's effectiveness and superiority.

Statistical learning based blind image watermarking approach

Image watermarking technology poses a significant challenge, requiring a delicate balance between robustness, imperceptibility, and capacity. To solve the balancing problem, a statistical learning based blind image watermarking technique is proposed in this paper. The idea of the proposed method to solve the balancing problem is to enhance imperceptibility and robustness while accommodating sufficient watermarks. Firstly, the local low-order pseudo-Zernike moments (PZM) magnitude in the discrete non-separable Shearlet transform (DNST) domain is constructed as the embedding domain. To ensure stability in embedding positions, we propose an edge embedding strategy. Secondly, by analyzing the statistical characteristics and multi-correlations of DNST-PZM magnitudes, a multi-correlation vector model based on the skew student's-t mixture (SSM) and hidden Markov tree (HMT) is designed. Finally, leveraging the vector SSM-HMT model and the maximum likelihood (ML) criterion, we derive the closed-form decoder expression. Comparative analysis with state-of-the-art methods demonstrates the superior imperceptibility and robustness of our proposed approach in accommodating the same capacity watermark.

Optimal position and dimensions of embedded normal piezoelectric transducers, higher order plate models and experimental approach

This study investigates the different optimal parameters of piezoelectric transducers polarized in the thickness direction. The studied parameters are regarding sensor and actuator embedding position in the thickness, sensor ratio and sensor dimensions. It involves a parametric study using MUL2 FE numerical tool, via coupled electro-mechanical two-dimensional models. The performance of the corresponding configuration was judged by the maximum output electric potential signal induced in the sensor due to the propagated Lamb Wave induced by the actuator. An experimental campaign was carried out on glass fiber/ polyester plates embedded with piezoelectric transducers to validate the different results.

ENHANCING MEDICAL DATA SECURITY IN E-HEALTH SYSTEMS USING BIOMETRIC-BASED WATERMARKING

In the field of Electronic Health (e-Health), Electronic Health Records (EHR) are transmitted between health professionals using e-Health systems for cooperative medical practice, medical monitoring, telemedical expertise, and telemedical imaging. Medical images are a crucial component of EHR and are used in various aspects of telemedicine systems such as expertise, consultation, teaching, and research. However, protecting the authenticity and copyrights of medical images is essential to prevent duplication, modification, or unauthorized distribution. This paper proposes a robust medical image copyright protection method that uses patient palm-print template as watermark and Lorenz chaotic map for template concealing and selecting the appropriate embedding positions in medical images. The novelty of the method lies in optimizing the expected number of modifications per pixel of the medical images after being watermarked. Experimental results indicate that this approach has a high performance with a genuine accept rate of 99.86% and can withstand various image processing attacks, including Gaussian noise, compression, and image rotations, while ensuring personal data security during telemedicine data exchange.

A reversible natural language watermarking for sensitive information protection

Existing methods have evolved from using synonym substitution to incorporating arbitrary word substitution to achieve reversible natural language watermarking. However, a notable limitation is that they are prone to overlook the sensitivity of information associated with the original words, with a tendency to prefer non-sensitive words for substitution. As a result, a potential risk of sensitive information leakage contained in the original text is posed. Furthermore, while aiming for reversibility, the overall performance of the watermarking method may be inadvertently compromised. In response to the above problems, this paper puts forward a novel reversible natural language watermarking method that combines a Keyword Substitution scheme and a Prediction Error Expansion algorithm (KSPEE) to protect sensitive information, verify content integrity, protect copyright, and so on. Specifically, KSPEE leverages a keyword extraction algorithm to identify important content containing sensitive information in the original text, thereby determining the potential positions for watermark information embedding. Subsequently, a masked language model is utilized to predict appropriate substitution words based on the surrounding semantic information of the embedding position. In addition, the prediction error expansion algorithm is employed to select appropriate words for substituting the original keywords, ensuring the successful embedding of watermark information while maintaining the recoverability of the original keywords. By identifying keywords and substituting them, a suitable method of protecting the original sensitive information is provided. Extensive experiments demonstrate that, under the promise of semantic distortion and lossless restoration of the original content, the proposed method KSPEE achieves outstanding watermarked text quality. A higher watermark embedding rate is achieved and strong security is shown by KSPEE. More importantly, KSPEE effectively prevents the leakage of sensitive information.

A robust blind color watermarking algorithm based on the Radon-DCT transform

Digital watermarking is an effective technique for image copyright protection. Many digital image watermarking algorithms are sensitive to geometric distortions. These distortions make it difficult to detect and extract watermarks. In this study, a robust color watermarking algorithm combining the Radon transform and DCT transform is proposed. First, the color carrier image is converted to the YUV color space from RGB, and the U component of YUV is transformed to the Radon domain using the Radon transform. Then, a two-dimensional 8 × 8 discrete cosine transform (2D-DCT) is carried out on the selected blocks of the Radon domain, and some fixed midfrequency coefficients are selected to embed watermark information. Moreover, the Arnold transform is applied to encrypt the watermarks and the random permutation function to scramble the embedding positions. Finally, the watermark embedding and blind extraction processes are completed by modifying the midfrequency coefficients using the presented rules. A series of simulation experiments show that the watermark algorithm has high imperceptibility and good robustness to various attacks.

Learning discriminative context for salient object detection

Context understanding is important for salient object detection (SOD) in complex scenes. To alleviate visual confusion, we propose a context aware network that full use of the same type contextual information for SOD. Specifically, we introduce the pixel relationships into decoder, which can extract the explicit contextual information to alleviate visual confusion by learning the similarities and differences between pixels. Furthermore, we explore the optimal embedding position of the pixel relationship in network to maximize its benefits, thereby reducing the introduction of noise. Compared with 20 counterparts, experimental results on five datasets show that our approach has better generalization, which averagely improves 1.3% and 2.8% over the advanced CNN-based model and Transformer-based model on three evaluation metrics. The improvements demonstrate effectiveness of the proposed context learning strategies, which are helpful for dealing with various complex scenes. Moreover, our model is more efficient with an inference speed of 56.7 FPS on a single NVIDIA 2080TI GPU. Codes are available at https://github.com/lesonly/CANet.

Analysis of Magneto-Mechanical Properties at the Tooth-Yoke Embedding Position in Grain-Oriented Electrical Steel Electric Motors

Analysis of Magneto-Mechanical Properties at the Tooth-Yoke Embedding Position in Grain-Oriented Electrical Steel Electric Motors

Visible watermarking for 3D models based on 3D Boolean operation

With the continuous expansion of the application field of 3D models and the convenience of model storage, transmission, and replication, the problems of illegal reproduction, illegal modification, and embezzlement faced by 3D models are gradually becoming severe, which has brought substantial economic losses to the producers of 3D models. Therefore, this study aims to develop a visible watermarking method for 3D model using 3D Boolean operation. First, based on TrueType font library, the 3D model of the watermark is generated through the extraction, positioning and combination of character outline. Next, the spatial registration between the watermark model and the 3D model was carried out according to the embedding position of the watermark. Finally, based on multiple 3D Boolean operations, the copyright information of the 3D watermark model is embedded into the 3D model in various modes, such as embossing, debossing, and surface mesh embedding. The experiments demonstrate that the method of this paper supports embedding watermark information in B-rep, CSG, and voxel models. This method can readily support business applications for visible watermarking of 3D models and has laid a good foundation for researching multipurpose 3D model watermarking algorithms supporting copyright notification and protection.

Security Protection of 3D Models of Oblique Photography by Digital Watermarking and Data Encryption

To clarify the copyrights of 3D models of oblique photography (3DMOP) and guarantee their security, a novel security protection scheme of 3DMOP was proposed in this study by synergistically applying digital watermarking and data encryption. In the proposed scheme, point clouds were clustered first, and then the centroid and feature points of each cluster were calculated and extracted, respectively. Afterward, the watermarks were embedded into the point clouds cluster-by-cluster, taking distances between feature points and centroids as the embedding positions. In addition, the watermarks were also embedded using texture coordinates of 3DMOP to further enhance the robustness of the watermarking algorithm. Furthermore, Arnold transformation was performed on texture images of 3DMOP for security protection of classified or sensitive information. Experimental results have verified the strong imperceptibility and robustness of the proposed watermarking algorithm, as well as the high security of the designed data encryption algorithm. The outcomes of this work can refine the current security protection methods of 3DMOP and thus further expand their application scope.

Optimization of facial expression recognition based on dual attention mechanism by lightweight network model

As a deep learning network model, ResNet50 can effectively recognize facial expressions to a certain extent, but there are still problems such as insufficient extraction of local effective feature information and a large number of parameters. In this paper, we take ResNet50 as the basic framework to optimize and improve this network. Firstly, by analyzing the influence mechanism of the attention mechanism module on the network feature information circulation, the optimal embedding position of CBAM (Convolutional Block Attention Module) and SE modules in the ResNet50 network is thus determined to effectively extract local key information, and then the number of model parameters is effectively reduced by embedding the depth separable module. To validate the performance of the improved ResNet50 model, the recognition accuracy reached 71.72% and 95.72% by ablation experiments using Fer2013 and CK+ datasets, respectively. We then used the trained model to classify the homemade dataset, and the recognition accuracy reached 92.86%. In addition, compared with the current more advanced methods, the improved ResNet50 network model proposed in this paper can maintain a balance between model complexity and recognition ability and can provide a technical reference for facial expression recognition research.