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Digital Image Steganographer Identification: A Comprehensive Survey

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Computers, Materials and Continua 2024年第1期81卷 105-131页

作者： Zhang Q. Zhang y. Ma y. Liu y. Luo X. Information Engineering University Zhengzhou 450001 China Key.Laboratory.of Cy.erspace Situation Awareness of Henan Province Zhengzhou 450001 China College of Computer and Information Engineering Henan Normal University Xinxiang 453007 China

The rapid development of the internet and digital media has provided convenience while also posing a potential risk of steganography.abuse. Identify.ng steganographer is essential in tracing secret information origins and preventing illicit covert communication online. Accurately.discerning a steganographer from many.normal users is challenging due to various factors, such as the complexity.in obtaining the steganography.algorithm, extracting highly.separability.features, and modeling the cover data. After extensive exploration, several methods have been proposed for steganographer identification. This paper presents a survey.of existing studies. Firstly. we provide a concise introduction to the research background and outline the issue of steganographer identification. Secondly. we present fundamental concepts and techniques that establish a general framework for identify.ng steganographers. Within this framework, state-of-the-art methods are summarized from five key.aspects: data acquisition, feature extraction, feature optimization, identification paradigm, and performance evaluation. Furthermore, theoretical and experimental analy.es examine the advantages and limitations of these existing methods. Finally. the survey.highlights outstanding issues in image steganographer identification that deserve further research. © 2024 The Authors. Published by.Tech Science Press.

关键词： covert communication Information hiding steganaly.is steganographer identification steganography survey

Graph Attention Residual Network Based Routing and Fault-Tolerant SchedulingMechanism for Data Flow in Power Communication Network

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Computers, Materials and Continua 2024年第1期81卷 1641-1665页

作者： Lin Z. Zeng Z. y. y. Ren y. Qiu X. Chen J. State Key.Laboratory.ofNetworking and Switching Technology Beijing University of Posts and Telecommunications Beijing 100876 China State Grid Jiangsu Electric Power Co. Ltd Nanjing 210008 China

For permanent faults (PF) in the power communication network (PCN), such as link interruptions, the timesensitive networking (TSN) relied on by.PCN, ty.ically.employ. spatial redundancy.fault-tolerance methods to keep service stability.and reliability. which often limits TSN scheduling performance in fault-free ideal states. So this paper proposes a graph attention residual network-based routing and fault-tolerant scheduling mechanism (GRFS) for data flow in PCN, which specifically.includes a communication sy.tem architecture for integrated terminals based on a cy.lic queuing and forwarding (CQF) model and fault recovery.method, which reduces the impact of faults by.simplified scheduling configurations of CQF and fault-tolerance of prioritizing the rerouting of faulty.time-sensitive (TS) flows; considering that PF leading to changes in network topology.is more appropriately.solved by.doing routing and time slot injection decisions hop-by.hop, and that reasonable network load can reduce the damage caused by.PF and reserve resources for the rerouting of faulty.TS flows, an optimization model for joint routing and scheduling is constructed with scheduling success rate as the objective, and with traffic latency.and network load as constraints; to catch changes in TSN topology.and traffic load, a D3QN algorithm based on a multi-head graph attention residual network (MGAR) is designed to solve the problem model, where the MGAR based encoder reconstructs theTSNstatus into feature embedding vectors, and a dueling network decoder performs decoding tasks on the reconstructed feature embedding vectors. Simulation results show that GRFS outperforms heuristic fault-tolerance algorithms and other benchmark schemes by.approximately.10%in routing and scheduling success rate in ideal states and 5% in rerouting and rescheduling success rate in fault states. © 2024 The Authors. Published by.Tech Science Press.

关键词： deep reinforcement learning fault tolerance graph attention network Time-sensitive network

A Facial Expression Recognition Method Integrating Uncertainty.Estimation and Active Learning

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Computers, Materials and Continua 2024年第1期81卷 533-548页

作者： Wang y. Zhang J. Sun R. School of Computer Science and Engineering Chongqing University of Technology Chongqing 400054 China Data Space Research Institute of Hefei Comprehensive National Science Center Hefei 230088 China

The effectiveness of facial expression recognition (FER) algorithms hinges on the model’s quality.and the availability.of a substantial amount of labeled expression data. However, labeling large datasets demands significant human, time, and financial resources. Although active learning methods have mitigated the dependency.on extensive labeled data, a cold-start problem persists in small to medium-sized expression recognition datasets. This issue arises because the initial labeled data often fails to represent the full spectrum of facial expression characteristics. This paper introduces an active learning approach that integrates uncertainty.estimation, aiming to improve the precision of facial expression recognition regardless of dataset scale variations. The method is divided into two primary.phases. First, the model undergoes self-supervised pre-training using contrastive learning and uncertainty.estimation to bolster its feature extraction capabilities. Second, the model is fine-tuned using the prior knowledge obtained from the pre-training phase to significantly.improve recognition accuracy. In the pre-training phase, the model employ. contrastive learning to extract fundamental feature representations from the complete unlabeled dataset. These features are then weighted through a self-attention mechanism with rank regularization. Subsequently. data from the low-weighted set is relabeled to further refine the model’s feature extraction ability. The pre-trained model is then utilized in active learning to select and label information-rich samples more efficiently. Experimental results demonstrate that the proposed method significantly.outperforms existing approaches, achieving an improvement in recognition accuracy.of 5.09% and 3.82% over the best existing active learning methods, Margin, and Least Confidence methods, respectively. and a 1.61% improvement compared to the conventional segmented active learning method. © 2024 The Authors.

关键词： active learning Expression recognition self-supervised learning uncertainty.estimation

Robust Deep Image Watermarking: A Survey

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Computers, Materials and Continua 2024年第1期81卷 133-160页

作者： Luo y. Tan X. Cai Z. College of Computer National University of Defense Technology Changsha 410005 China

In the era of internet proliferation, safeguarding digital media copy.ight and integrity. especially.for images, is imperative. Digital watermarking stands out as a pivotal solution for image security. With the advent of deep learning, watermarking has seen significant advancements. Our review focuses on the innovative deep watermarking approaches that employ.neural networks to identify.robust embedding spaces, resilient to various attacks. These methods, characterized by.a streamlined encoder-decoder architecture, have shown enhanced performance through the incorporation of novel training modules. This article offers an in-depth analy.is of deep watermarking’s core technologies, current status, and prospective trajectories, evaluating recent scholarly.contributions across diverse frameworks. It concludes with an overview of the technical hurdles and prospects, providing essential insights for ongoing and future research endeavors in digital image watermarking. © 2024 The Authors. Published by.Tech Science Press.

关键词： data protection deep neural network Deep image watermarking multimedia security

Research on Defect Detection ofWind Turbine Blades Based onMorphology.and Improved Otsu Algorithm Using Infrared Images

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Computers, Materials and Continua 2024年第1期81卷 933-949页

作者： Kang S. He y. Li W. Liu S. School of Mechanical and Control Engineering Baicheng Normal University Baicheng 137000 China School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin 132022 China

To address the issues of low accuracy.and high false positive rate in traditional Otsu algorithm for defect detection on infrared images of wind turbine blades (WTB), this paper proposes a technique that combines morphological image enhancement with an improved Otsu algorithm. First, mathematical morphology.s differential multi-scale white and black top-hat operations are applied to enhance the image. The algorithm employ. entropy.as the objective function to guide the iteration process of image enhancement, selecting appropriate structural element scales to execute differential multi-scale white and black top-hat transformations, effectively.enhancing the detail features of defect regions and improving the contrast between defects and background. Afterwards, gray.cale inversion is performed on the enhanced infrared defect image to better adapt to the improved Otsu algorithm. Finally. by.introducing a parameter K to adjust the calculation of inter-class variance in theOtsumethod, theweight of the target pixels is increased. Combined with the adaptive iterative threshold algorithm, the threshold selection process is further fine-tuned. Experimental results show that compared to traditional Otsu algorithms and other improvements, the proposed method has significant advantages in terms of defect detection accuracy.and reducing false positive rates. The average defect detection rate approaches 1, and the average Hausdorff distance decreases to 0.825, indicating strong robustness and accuracy.of the method. © 2024 The Authors.

关键词： adaptive iterative thresholding gray.cale inversion improved Otsu algorithm infrared image Morphological enhancement

Paraelectric Doping Simultaneously.Improves the Field Frequency.Adaptability.and Dielectric Properties of Ferroelectric Materials: A Phase-Field Study

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Computers, Materials and Continua 2024年第1期81卷 213-228页

作者： Wang Z. Cao J. Liu Z. Zhao y. School of Materials Science and Engineering Collaborative Innovation Center of Ministry of Education and Shanxi Province for High-Performance Al/Mg Alloy Materials North University of China Taiyuan 030051 China Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing 100083 China Institute of Materials Intelligent Technology Liaoning Academy of Materials Shenyang 110004 China

Recent y.ars, the polarization response of ferroelectrics has been entirely.studied. However, it is found that the polarization may.disappear gradually.with the continually.applied of electric field. In this paper, taking K0.48Na0.52NbO3(KNN) as an example, it was demonstrated that the residual polarization began to decrease when the electric field frequency.increased to a certain extent using a phase-field methods. The results showed that the content of out-of-plane domains increased first and then decreased with the increase of applied electric field frequency. the maximum polarization disappeared at high frequencies, and the hy.teresis loop became elliptical. In order to further study.the abnormal changes of hy.teresis loops of ferroelectrics under high electric field frequency. we analy.ed the hy.teresis loop and dielectric response of solid solution 0.1SrTiO3-0.9K0.48Na0.52NbO3. It was found that the doped hy.teresis loop maintained its shape at higher frequency.and the dielectric constant increased. This kind of doping has a higher field frequency.adaptability. which has a key.guiding role in improving the dielectric properties of ferroelectric thin films and expanding the frequency.application range of ferroelectric nano memory. © 2024 The Authors. Published by.Tech Science Press.

关键词： dielectric properties doping electric field frequency Ferroelectric ceramics phase field method

APSO-CNN-SE: An Adaptive Convolutional Neural Network Approach for IoT Intrusion Detection

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Computers, Materials and Continua 2024年第1期81卷 567-601页

作者： Ban y. Zhang D. He Q. Shen Q. College of Big Data and Information Engineering Guizhou University Guizhou 550025 China

The surge in connected devices and massive data aggregation has expanded the scale of the Internet of Things (IoT) networks. The proliferation of unknown attacks and related risks, such as zero-day.attacks and Distributed Denial of Service (DDoS) attacks triggered by.botnets, have resulted in information leakage and property.damage. Therefore, developing an efficient and realistic intrusion detection sy.tem (IDS) is critical for ensuring IoT network security. In recent y.ars, traditional machine learning techniques have struggled to learn the complex associations between multidimensional features in network traffic, and the excellent performance of deep learning techniques, as an advanced version of machine learning, has led to their widespread application in intrusion detection. In this paper, we propose an Adaptive Particle Swarm Optimization Convolutional Neural Network Squeeze-and-Excitation (APSO-CNN-SE) model for implementing IoT network intrusion detection. A 2D CNN backbone is initially.constructed to extract spatial features from network traffic. Subsequently. a squeeze-and-excitation channel attention mechanism is introduced and embedded into the CNN to focus on critical feature channels. Lastly. the weights and biases in the CNN-SE are extracted to initialize the population individuals of the APSO. As the number of iterations increases, the population’s position vector is continuously.updated, and the cross-entropy.loss function value is minimized to produce the ideal network architecture. We evaluated the models experimentally.using binary.and multiclassification on the UNSW-NB15 and NSL-KDD datasets, comparing and analy.ing the evaluation metrics derived from each model. Compared to the base CNN model, the results demonstrate that APSO-CNN-SE enhances the binary.classification detection accuracy.by.1.84% and 3.53% and the multiclassification detection accuracy.by.1.56% and 2.73% on the two datasets, respectively. Additionally. the model outperforms the

关键词： adaptive particle swarm optimization channel attention mechanism convolutional neural network internet of things Intrusion detection sy.tem

Improved Harris Hawks Algorithm and Its Application in Feature Selection

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Computers, Materials and Continua 2024年第1期81卷 1251-1273页

作者： Zhang Q. Li y. Zhan J. Chen S. School of Computer Science and Information Engineering Harbin Normal University Harbin 150025 China College of Sy.tems Engineering National University of Defense Technology Changsha 410073 China

This research focuses on improving the Harris' Hawks Optimization algorithm (HHO) by.tackling several of its shortcomings, including insufficient population diversity. an imbalance in exploration vs. exploitation, and a lack of thorough exploitation depth. To tackle these shortcomings, it proposes enhancements from three distinct perspectives: an initialization technique for populations grounded in opposition-based learning, a strategy.for updating escape energy.factors to improve the equilibrium between exploitation and exploration, and a comprehensive exploitation approach that utilizes variable neighborhood search along with mutation operators. The effectiveness of the Improved Harris Hawks Optimization algorithm (IHHO) is assessed by.comparing it to five leading algorithms across 23 benchmark test functions. Experimental findings indicate that the IHHO surpasses several contemporary.algorithms its problem-solving capabilities. Additionally. this paper introduces a feature selection method leveraging the IHHOalgorithm(IHHO-FS) to address challenges such as lowefficiency.in feature selection and high computational costs (time to find the optimal feature combination and model response time) associated with high-dimensional datasets. Comparative analy.es between IHHO-FS and six other advanced feature selection methods are conducted across eight datasets. The results demonstrate that IHHO-FS significantly.reduces the computational costs associated with classification models by.lowering data dimensionality. while also enhancing the efficiency.of feature selection. Furthermore, IHHO-FS shows strong competitiveness relative to numerous algorithms. © 2024 The Authors. Published by.Tech Science Press.

关键词： deep exploitation strategy energy.factor update strategy feature selection HHO IHHO population diversity

Re-Distributing Facial Features for Engagement Prediction with ModernTCN

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Computers, Materials and Continua 2024年第1期81卷 369-391页

作者： Li X. Zhu W. Li Q. Hou C. Zhang y. College of Information and Artificial Intelligence Nanchang Institute of Science and Technology Nanchang 330108 China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan 430205 China School of Electronic Information Engineering Wuhan Donghu University Wuhan 430212 China

Automatically.detecting learners’ engagement levels helps to develop more effective online teaching and assessment programs, allowing teachers to provide timely.feedback and make personalized adjustments based on students’ needs to enhance teaching effectiveness. Traditional approaches mainly.rely.on single-frame multimodal facial spatial information, neglecting temporal emotional and behavioural features, with accuracy.affected by.significant pose variations. Additionally. convolutional padding can erode feature maps, affecting feature extraction’s representational capacity. To address these issues, we propose a hy.rid neural network architecture, the redistributing facial features and temporal convolutional network (RefEIP). This network consists of three key.components: first, utilizing the spatial attention mechanism large kernel attention (LKA) to automatically.capture local patches and mitigate the effects of pose variations; second, employ.ng the feature organization and weight distribution (FOWD) module to redistribute feature weights and eliminate the impact of white features and enhancing representation in facial feature maps. Finally. we analy.e the temporal changes in video frames through the modern temporal convolutional network (ModernTCN) module to detect engagement levels. We constructed a near-infrared engagement video dataset (NEVD) to better validate the efficiency.of the RefEIP network. Through extensive experiments and in-depth studies, we evaluated these methods on the NEVD and the Database for Affect in Situations of Elicitation (DAiSEE), achieving an accuracy.of 90.8% on NEVD and 61.2% on DAiSEE in the four-class classification task, indicating significant advantages in addressing engagement video analy.is problems. © 2024 The Authors. Published by.Tech Science Press.

关键词： Engagement prediction re-distributing facial features spatiotemporal network temporal convolutional network