Highlights in Science, Engineering and Technology
Highlights in Science, Engineering and Technology is an open access, peer-reviewed publication series published by Darcy & Roy Press. It aims to publish conference proceedings dealing with all areas of science, engineering and technology.
Art And Technology Integration: CNN-Based Image Style Transfer Research
This research presents a CNN-based image style transfer framework that integrates layered feature extraction with region-aware stylization. Traditional neural style transfer methods often apply styles globally across an image, blurring boundaries between subjects and backgrounds and reducing fine-grained stylistic control. To address this limitation, the proposed method combines multi-layer feature modeling using the VGG19 network with semantic segmentation from DeepLabv3 to distinguish subject and background regions. By separating feature maps using binary masks, the system computes independent style losses for each region while maintaining the overall content structure via a content loss function. Experimental results on a dataset of diverse images—including portraits, architecture, and landscapes—demonstrate that the framework achieves clearer structural preservation and more balanced stylization than conventional global methods. Parameter studies further show that adjusting the weights of content, subject style, and background style enables flexible artistic control. The approach offers potential applications in digital art creation, cultural heritage visualization, and AI-assisted design.
Neural Style Transfer; Convolutional Neural Networks; Semantic Segmentation.
Copyright (c) 2026 Highlights in Science, Engineering and Technology https://creativecommons.org/licenses/by-nc/4.0
Comprehensive Evaluation and Planning Optimization of Urban Development Levels in Xinjiang Based on POI Data
This study constructs a multidimensional mathematical model for evaluating and optimizing the development level of cities in Xinjiang based on multi-source data. For housing price prediction and existing housing stock estimation, a multi-factor linear prediction model is established. This model comprehensively considers four major factors-economic, service, location, and policy-with corresponding weight coefficients assigned: the economic factor carries a weight of 0.35, and the service factor a weight of 0.25. Model solutions predict housing price ranges for major Xinjiang cities between 5,668-7,308 RMB/m², with an average forecast of 5,379 RMB/m². The total existing housing stock is estimated at approximately 73.32 million units. For quantifying and classifying urban service levels, a multidimensional evaluation system encompassing four core domains—healthcare, education, transportation, and commerce-was constructed. Principal component analysis was applied to reduce the dimensionality of the service level matrix, revealing that the first principal component explained 99.93% of the variance, indicating high correlation among service domains. Subsequently, the K-means clustering algorithm categorized cities into four types: excellent service, good service, weak service, and developing service. For urban resilience assessment and investment demand planning, a four-dimensional resilience evaluation model was established, comprising infrastructure (weight 0.4), economy (weight 0.3), society (weight 0.2), and environment (weight 0.1). Based on the resilience index, an investment demand assessment model and a weakness identification algorithm were constructed. Model results indicate a total investment requirement of 259.8 billion yuan, with Urumqi achieving the highest comprehensive resilience index. Finally, for smart city development planning and benefit assessment, an evaluation model for current and target smart levels was established. Investment allocation ratios were determined across five key domains—smart transportation, smart government services, etc.—projecting over 50% improvement in urban development levels.
POI data, multi-factor prediction model, urban resilience evaluation.
Optimization Of Collaborative UAV Smoke Screen Deployment for Missile Defense Based on Intelligent Optimization
The increasing threat of precision-guided missiles to high-value assets necessitates the development of effective, dynamic countermeasures. Smoke screens, deployed by Unmanned Aerial Vehicles (UAVs), offer a flexible, low-cost, and highly effective means of obscuring targets from optical and infrared seekers. However, maximizing their defensive capability requires sophisticated spatiotemporal coordination. To address this challenge, this paper proposes a comprehensive optimization model for the intelligent deployment of UAV-launched smoke screens. This study establish a "Kinematics-Geometry-Optimization" framework, which integrates detailed mathematical models for the trajectories of UAVs, missiles, and descending smoke clouds. Intelligent optimization algorithms, specifically Particle Swarm Optimization (PSO) and Simulated Annealing (SA), are employed to solve the high-dimensional problem of maximizing the total effective obscuration time across various combat scenarios. Simulations validate the model's efficacy, demonstrating a 213% increase in obscuration time in a single-asset scenario. In a complex engagement involving 5 UAVs defending against 3 missiles, the model successfully coordinates the deployment of 13 smoke bombs to achieve a total obscuration time of 100.22 seconds, forming a seamless, multi-layered defensive barrier. The proposed model provides a systematic, scalable, and computationally efficient solution for real-time tactical planning in missile defense.
UAV Swarm, Missile Defense, Multi-objective Optimization, Kinematic Modeling.
Cooperative Optimization of Multi-UAV Smoke Screen Deployment Based on Obscuration Cone Model
Aiming at the "multi-UAV-single missile" smoke screen jamming mission in complex battlefield environments, this paper proposes a three-UAV cooperative deployment optimization method based on the Obscuration Cone Model. By constructing the kinematic models of the missile, UAVs and smoke cloud, a geometric judgment equation of the obscuration cone is established, and the Differential Evolution Algorithm is adopted to solve the nonlinear optimization model containing 12 decision variables. The study presents a heuristic heading angle constraint screening strategy to reduce the search space with high precision (0.1 degrees angle, 0.001 seconds time step). Simulation results show that the method significantly improves the target obscuration duration, with a total effective obscuration time of 11.714 seconds, an increase of approximately 150% compared with the single-UAV strategy. The results verify the effectiveness of the Obscuration Cone Model and the cooperative optimization algorithm in tactical-level protection.
Obscuration Cone Model, Multi-UAV Coordination, Smoke Screen Jamming, Heuristic Constraints, Differential Evolution Algorithm.
Measurement Of Silicon Carbide Epitaxial Layer Thickness Based on Infrared Interferometry and Fourier Transform Analysis
To meet the demand for high-accuracy, non-destructive measurement of silicon carbide (SiC) epitaxial layer thickness, this paper presents a progressively refined infrared interferometry methodology. Starting from fundamental physical principles, this approach systematically addresses two major error sources in practical measurements—dynamic refractive index changes and multi-beam interference—by constructing increasingly sophisticated mathematical models. The main innovations of this work are: 1) To overcome the noise sensitivity of traditional extremum-based methods, a robust "Global Interference Order Alignment" algorithm is proposed, which utilizes the entire spectrum to achieve a reliable initial thickness estimation. 2) To resolve the systematic errors caused by multi-beam interference, a high-precision model based on the Fast Fourier Transform (FFT) is further developed. This advanced model extracts the optical path difference from the signal's global frequency characteristics, proving insensitive to non-sinusoidal fringe shape distortions. A Drude-Sellmeier hybrid model is integrated to accurately describe the material's optical properties, accounting for both lattice vibrations and free carriers. Experimental results show that while the initial global alignment algorithm demonstrates strong noise resistance (yielding a thickness of 9.9005 µm), it exhibits a notable discrepancy between measurements at different angles. The advanced FFT model, after confirming the presence of multi-beam interference via kurtosis analysis, effectively eliminates the associated systematic errors. It yields a revised and more accurate thickness of 11.0616 µm for the SiC epitaxial layer, with significantly enhanced consistency and stability across different incident angles. This study not only provides a high-accuracy and highly stable measurement solution for industrial quality control of SiC epitaxial layers but also offers a valuable reference for other spectral metrology fields with its systematic, multi-stage approach to solving complex physical problems.
Infrared Interferometry, Drude-Sellmeier Hybrid Model, Fast Fourier Transform, Extremum Point Localization.
Research On an Optimal Nipt Timing Decision Model Driven by Multidimensional Factors Based on Machine Learning
The accuracy of non-invasive prenatal testing (NIPT) is highly dependent on selecting the optimal testing time, which varies due to individual differences among pregnant women. To address the limitations of the traditional “one-size-fits-all” testing approach, this paper constructs an intelligent model framework that integrates prediction, stratification, and decision-making. First, the framework employs a CatBoost regression model to accurately predict the key indicator of fetal cell-free DNA (cffDNA)—Y chromosome concentration—based on multidimensional physiological indicators of pregnant women, achieving a coefficient of determination (R²) of 0.717 on the test set. Next, a K-means clustering algorithm is used to achieve data-driven refined stratification of the study population, and principal component analysis (PCA) is applied to process high-dimensional features. Building on this, the study innovatively introduces a Cox proportional hazards model to construct a risk function and establishes an optimization model to calculate personalized optimal NIPT timing for different stratified groups of pregnant women. For example, in the comprehensive multi-factor model, the optimal testing times determined for the low-BMI and high-BMI groups were 11.997 weeks and 10.420 weeks, respectively. In addition, to address the issue of detecting female fetal chromosomal aneuploidy, a decision tree classification model was developed, achieving an average accuracy of 0.886 with five-fold cross-validation. The core contribution of this study lies in the deep integration of machine learning prediction models with operational optimization and decision models, achieving a paradigm shift from “passive prediction” to “active decision-making,” and providing a scientific, efficient, and individualized decision support solution for clinical NIPT practice.
Machine learning, CatBoost regression, Cox regression, K-means clustering, decision optimization.
Research On Intelligent Body Measurement Algorithm Based on AI Attitude Recognition
To solve the problems of difficult quantitative analysis of standing long jump movements, unclear factors affecting performance, and lack of personalized training programs, this paper conducts research based on AI human posture estimation technology to obtain key node coordinate data of athletes. To address the challenges of data noise and action stage segmentation, sliding window smoothing is used to process data, and dynamic ground baselines are constructed by combining foot nodes. Weighted fusion features are used to accurately determine takeoff and landing times, and the hovering process is quantified from dimensions such as center of gravity displacement and joint angles; To meet the needs of performance prediction and training optimization, a prediction model is constructed based on gradient boosting regression. The simulation results show a high accuracy in predicting the actual long jump performance of athletes. In the field of sports, the research findings of this article can be extended to other track and field events such as high jump, throwing, and sprinting, as well as the analysis of technical movements in ball sports such as basketball and football.
Smooth Sliding Window, Lleave One Cross Validation, Gradient Boosting Regression.
Deep Engagement, Real Purchases: Psychological Mechanisms Linking Social Media Engagement to Physical Music Consumption
Against the background of the high popularity of digital music, the sustainable value of physical music products in the fan economy still needs to be explained in depth. Based on the use of social media, this article distinguishes the depth of fans' social media engagement, and introduces social motivation, emotional motivation and collection motivation as psychological mechanisms, and systematically examines how the depth of engagement affects the purchase behavior of physical music. On the basis of questionnaire data, PCA and Varimax, multivariate linear regression analysis, Bootstrap confidence interval method, and regulatory regression model, the following conclusions are drawn. Compared with low engagement, deep social media engagement has a significant and stronger positive impact on physical music purchasing behavior; social motivation and emotional motivation play a significant intermediary role between the depth of engagement and purchasing behavior, while collection motivation does not form a stable intermediary path; singer interaction perception does not significantly regulate the depth of engagement on psychology. The robustness test further verified the universality of the research conclusions. This study expands the research perspective of fans' consumption behavior from the perspective of engagement level and psychological mechanism, and provides inspiration for the fan operation practice of the music industry.
Social media engagement depth; fan consumption behaviour; physical music purchase; mediation effect; parasocial interaction.
Advances And Prospects of Cellular Automata in Simulating Spatial Expansion of Metropolitan Areas
This study systematically reviews research advances in Cellular Automata (CA) modeling for spatial expansion simulation. Bibliometric and model evolution analyses reveal an evolutionary trajectory of Cellular Automata models transitioning from basic morphological simulation to "multi-factor coupling, uncertainty quantification, and intelligent optimization". Methodological innovations in resolving the "high-density agglomeration–resource overload" paradox are examined, focusing on three directions: ecological constraint integration, deep learning optimization, and uncertainty quantification. Following the logical framework of "technological evolution–scale adaptation–paradigm innovation", future breakthroughs are proposed addressing bottlenecks in scale effects and social mechanism integration, thereby providing scientific support for territorial spatial planning. In addition, the review emphasizes the necessity of integrating cross-disciplinary methods to enhance the explanatory power of CA models. It also highlights the importance of policy-oriented applications in guiding sustainable urban expansion. Ultimately, the study aims to bridge theoretical modeling with practical governance, ensuring CA research contributes effectively to real-world planning challenges.
Cellular Automata; Spatial Expansion; Simulation; Urban Expansion.
An Investigation into The Application of Function Fitting in Trend Analysis
Trend information of discrete observations is an important input in science in data-driven environments. The following paper critically analyzes the usage value and risks of trend analysis with use of function fitting. To begin with, it classifies the five most popular fitting techniques, including linear, non-linear (polynomial), exponential, periodic, and power functions and it builds a method selection system ranging between easy and complicated. It then compares the effectiveness in various fitting schemes using stock prices, temperature changes, and drug pharmacokinetics as case studies to find the practical use of various fitting schemes in trend extraction, volatility smoothing and predictive accuracy enhancement. The results show that accuracy fitting depends on the synergies of the model complexity, data quality and domain knowledge and that overfitting and homoscedasticity and structure misidentification are important contributors to poor extrapolation reliability. The conclusion verifies that functional fitting is an important instrument that cannot be neglected in the trend analysis but its successful usage depends on correct perception of the peculiarities of the methods, strict control of the absence of data, and thorough combination with professional processes. The next efforts will involve machine learning and uncertainty quantification methods together in order to create a more robust and interpretable intelligent fitting algorithm, which can meet the needs of decision-making in high-dimensional complex data.
Function Fitting; Trend Analysis; Application Scenarios.
Analysis And Comparison on Image-Based Fatigue State Detection Methods
Nowadays, fatigue driving has become one of the main causes of road traffic accidents worldwide. To ensure people's safety, the academic community has continuously proposed and applied new fatigue detection methods, but there are still many limitations in current research. Manual extraction of fatigue features cannot obtain more detailed information; individual differences among different subjects may lead to reduced accuracy of fatigue detection; the detection delay is high, and the real-time performance needs to be improved...In response, this paper presents a comparative review of image-based fatigue detection methods. It systematically analyzes recent advances by comparing their core processes, including datasets, feature extraction techniques (both handcrafted and deep learning-based), and classification models. This research finds that Convolutional Neural Network(CNN) has gradually matured in the feature extracting domain. Many studies have built upon this foundation, introducing improvements and innovations. These studies extract features through machine learning and deep learning, and perform fatigue state classification using various combined models. The innovation of personalized thresholds allow flexible adjustment of judgment threshold according to each person's characteristics to increase accuracy, and the introduction of OpenCV, Dlib libraries and YOLO models has significantly enhanced real-time performance... The systematic summary provided in this study offers support for researchers to further optimize fatigue detection models, and also provides fatigue detection references for car manufacturers and high-risk operation industries. These contributions are conducive to ensuring the safety of relevant personnel.
Fatigue detection; Computer vision; Deep learning; Feature fusion; Facial landmarks.
Time Series Analysis Based on Mixed-Effects Modeling and LSDV Estimation
This paper proposes a modeling and analysis method based on a mixed-effects framework to address sudden performance improvements in complex systems. The core of this research lies in identifying and quantifying structural transitions embedded within time-series data through appropriate statistical and computational models. First, a breakpoint detection mechanism is designed to extract latent structural break signals from multidimensional time series, thereby filtering out sample sets exhibiting significant change characteristics. Second, at the model construction level, a mixed-effects modeling approach is introduced. This integrates key control factors and core state variables into a unified regression framework, while incorporating random disturbance terms to characterize heterogeneity differences, ensuring the model's interpretability and robustness. Finally, to address limitations in fixed-effects control, this study employs least squares dummy variable (LSDV) estimation. This technique transforms unobservable long-term differences into explicit dummy parameters for modeling, effectively mitigating omitted variable bias. The results demonstrate that this method can reliably identify and quantify structural transition effects across diverse scenarios, showcasing strong generalizability and practical applicability.
Mutation point detection; mixed-effects model; least squares dummy variable (LSDV).
Optimization of Classical Cryptography by Modern Programming Languages
The Caesar cipher’s vulnerability stems from its preservation of linguistic patterns, enabling a systematic decryption approach. By leveraging Python, the cracking process efficiently tests all possible shifts through automated decryption. Each attempted solution undergoes dual evaluation, statistical frequency analysis measures alignment with characteristic English letter distributions, while dictionary validation confirms the presence of legitimate vocabulary. These complementary techniques generate reliability scores for every candidate text. Moreover, this study extends the analysis to the Vigenère cipher and exploring how its polyalphabetic structure interacts with computational attacks. Ultimately, the highest-scoring outcome reliably identifies both the original messages and encryption key, demonstrating how computational power combined with linguistic insight can overcome classical cryptography’s limitations through methodical analysis rather than mere brute force, and paving the way for understanding how modern programming languages can optimize and challenge classical cryptographic systems.
Caesar cipher, Vigenère Cipher, Frequency Attack.
Research On Prevention and Control Mode and Governance Mechanism of Chronic Diseases Based on Medical Statistics
Faced with the triple challenges of high incidence, common comorbidity and heavy economic burden of chronic diseases in China, the traditional passive mode of "hospital-centered" is unsustainable. This study puts forward a research framework of "medical statistical data mining-dynamic prevention and control model-multi-collaborative governance", and systematically answers three scientific questions: "how to extract key features, how to achieve accurate stratification, and how to ensure sustainable governance". Based on more than 45,000 longitudinal queues of multi-center HIS/LIS/EHR, wearable devices and environmental monitoring, cross-domain data fusion is completed by using differential privacy (ε=1.5) and federated learning; Combined with random forest and XGBoost, the risk prediction model was established (AUC=0.82), and five heterogeneous groups were excavated by Apriori and K-means (35.7% in the elderly comorbid disability group and 18.5% in the environmentally sensitive hypertension group). Based on this, a data-driven prevention and control model of "accurate prediction-graded intervention-effect closed loop" is designed, and the long-term cost-effectiveness of 12 governance strategies is quantitatively compared on PEST-SD simulation platform. Further build a blockchain data sharing and performance compensation mechanism of "government-led, departmental collaboration, social participation and personal responsibility" to realize the governance paradigm transformation from "one-way decision-making by the government" to "multi-subject collaboration" and from "intervention after the event" to "full management". The research results provide a replicable Chinese plan for the reform of health insurance per head, the construction of healthy cities and the implementation of healthy China strategy.
prevention and control mode; governance mechanism; chronic diseases; medical statistics.
The Empowerment and Countermeasures of Artificial Intelligence in Internet of Things Security
Amid the backdrop of a surging number of Internet of Things (IoT) devices, a rapidly expanding attack surface, and the extension of security threats from the virtual realm to the physical world, traditional security mechanisms are confronted with severe challenges of insufficient scalability, real-time performance, and adaptability. Concurrently, the rapid development of artificial intelligence (AI) technology, particularly its advantages in pattern recognition, anomaly detection, and automated decision-making, offers new empowerment paths for IoT security protection. This research systematically reviews the key applications of AI in IoT security, including intelligent intrusion detection based on deep learning, continuous identity authentication based on behavior and radio frequency fingerprints, and privacy-enhanced threat detection relying on federated learning. These technologies have significantly improved the accuracy of threat identification, the real-time response, and the feasibility of cross-device collaborative defense. However, the research also delves into the new threat of "aggressive AI" formed by the exploitation of AI technology by attackers, including evading detection through generative adversarial networks, automated penetration based on reinforcement learning, and direct adversarial attacks against AI models themselves. This reveals that while AI enhances defense capabilities, it also introduces new vulnerabilities and adversarial dimensions. The conclusion of this study emphasizes that AI plays a dual role in IoT security, both empowering and attacking, driving the security game into a new stage of rapid escalation.
Intelligent defense, Adversarial challenges, Internet of Things.
Copyright (c) 2025 Highlights in Science, Engineering and Technology https://creativecommons.org/licenses/by-nc/4.0
Solving the KdV Equation Using the PINN Method
Traditional numerical methods such as the Finite Difference Method (FDM) often exhibit significant numerical dissipation and dispersion errors when solving the Korteweg-de Vries (KdV) equation, particularly in long-term simulations or complex wave interactions like double soliton collisions. To address these limitations, this study proposes an improved approach based on Physics-Informed Neural Networks (PINNs). By embedding the physical constraints of the KdV equation directly into the loss function, and employing a lightweight fully-connected neural network with four hidden layers and automatic differentiation, the method enhances both accuracy and generalization capability. Numerical experiments on single and double soliton cases demonstrate that the PINN method significantly outperforms FDM and conventional Artificial Neural Networks (ANNs). For instance, in the single soliton case, the maximum error of PINN is reduced to 8.36e-03, with an L2 error of 1.33e-02, while FDM results in larger errors due to numerical artifacts. In the double soliton collision scenario, PINN effectively captures the nonlinear interaction process with minimal error accumulation. Although slight discrepancies are observed near the wave crest-especially for the faster wave-the overall waveform and dynamic behavior are accurately preserved. The absolute error distribution further confirms the stability and precision of the PINN solution across the computational domain. These results underscore the critical role of physical constraints in improving the modeling of nonlinear wave phenomena. The proposed PINN framework offers a robust, efficient, and high-fidelity alternative for solving complex PDEs in scientific and engineering applications.
Physics-Informed Neural Networks, Korteweg-de Vries Equation, Soliton Wave.
Black Box Watermarking Technology of the AI Model
Today's rapidly developing AI models, black box watermarking technology is becoming an important means of intellectual property protection as it does not require access to the internal structure of the model. The theme of this paper is the black box watermarking technology of artificial intelligence models. First, the paper explains its theory, utilizing the ‘memory’ of the model to achieve identification through embedding and verification processes. Second, based on the different watermark embedding methods, two mainstream embedding methods are analyzed. In the training embedding stage, the model ‘remembers’ the mapping relationship, while in the fine-tuning stage, the watermark is embedded with a small number of parameters. Besides, this paper explores key performance indicators including robustness fidelity. Finally, for the current technological development situation, suggestions such as dynamically triggering sample generation are proposed, and future development trends are pointed out. This paper may help the researchers who are working in this field.
Assessment of Renal Artery Ablation Efficacy Based on Intravascular Ultrasound
Aiming at the problems that existing Renal Denervation (RDN) systems cannot evaluate ablation depth and whether renal sympathetic nerves are ablated, and that the Renal Nerve Stimulation (RNS) method has not entered the clinical application stage, this study proposes a method to assess the success of renal artery ablation surgery based on intravascular ultrasound observation. Leveraging the advantages of ultrasound in nerve imaging and the application characteristics of intravascular ultrasound, a 40 MHz high-frequency ultrasound imaging transducer and an ultrasonic ablation transducer were used. First, the transducer parameters were tested to ensure performance compliance. Subsequently, ultrasound imaging was performed on isolated porcine renal arteries to clarify the ultrasonic morphological characteristics of single renal sympathetic nerves, and ultrasound image changes before and after ablation were compared. On this basis, by observing post-ablation nerve image changes, the criteria for judging the feasibility and safety of ultrasonic ablation of renal artery sympathetic nerves were determined, which were then used to assess surgical success. Results show that the proposed method can clearly identify renal sympathetic nerves and has unique advantages in observing ablation effects, providing an effective basis for evaluating surgical implementation.
Intravascular Ultrasound Imaging, Renal Sympathetic Nerves, Ultrasound, Ablation.
Detection and Analysis of Human Mental State Based on Multimodal Information
With the continuous evolution and refinement of intelligent sensing technologies, the development and application of multimodal intelligent information in detecting human mental states have become a key direction in the advancement of intelligent technologies, surpassing traditional unimodal detection methods. Multimodal detection technology that integrates multiple information sources to reflect human mental states has been widely applied in fields such as medical care and interrogation. However, existing detection methods generally have disadvantages such as low precision and detection equipment being susceptible to environmental influences. Based on this background, this paper explores a framework for detecting and analyzing human mental states based on multimodal information. This framework integrates multi-source data, including non-contact sensing, contact-based physiological signal acquisition, and emerging dataset construction. This framework combines feature level, decision-making-level level and end-to-end multimodal fusion methods to achieve the deep synergy of cross-modal features. It enables the coordinated utilization of multiple human signal technologies, establishing a comprehensive data analysis framework for multimodal feature fusion. The rational use of this framework can significantly improve the robustness of the detection method. This study proposes innovative improvement solutions for applications in scenarios such as medical monitoring and judicial interrogations.
Human mental state, multimodal information, framework.
Progress and Challenges in Addressing Hallucination Issues in Large Language Models
The illusion problem of large language models refers to the phenomenon where the content generated by the model is inconsistent with the input information or objective facts, significantly limiting its reliability and safety. This paper systematically reviews the definition and classification of the illusion problem, including factual illusions and fidelity illusions, intrinsic and extrinsic illusions, as well as types of closed-domain and open-domain illusions. It also summarizes the current mainstream evaluation methods from three perspectives: data, models, and multi-task applications. Regarding mitigation strategies, the article proposes various technical paths at the data, model, and application levels, including data cleaning and augmentation, model architecture optimization, prompt engineering, and real-time retrieval-augmented generation, which effectively enhance the accuracy and consistency of the generated content. Future research should aim to establish a more refined evaluation system, promote collaborative optimization across multiple technologies, and achieve dynamic knowledge updates and lightweight deployment to strengthen the practicality and safety of large models in real-world scenarios.
Large Language Models, Hallucination Issues, Evaluation Methods, Mitigation Strategies.
Recent Advances in Copy–Move Forgery Detection: A Deep Learning Oriented Survey
With the widespread use of digital images in news reporting, forensic analysis, and social media, the problem of image tampering has become increasingly prominent. To address this, researchers have proposed various Copy–Move Forgery Detection (CMFD) approaches, primarily based on block segmentation, key points, and deep learning. However, with the rapid development of emerging technologies such as Transformers, Generative Adversarial Networks (GANs), and Diffusion Models, while exploratory research has emerged in image tampering detection, a systematic review of these approaches is lacking. Therefore, this article systematically reviews the development of CMFD within a three-part framework, focusing on the latest progress from 2023 to 2024. Specifically, this article reviews the classic contributions and evolution of block-based methods and analyzes the application value of key point-based methods in low-computing scenarios such as mobile devices and remote sensing. It also explores the advantages of deep learning methods in detection accuracy, robustness, and adaptability to AIGC scenarios. This paper further compares the characteristics and application scenarios of three types of methods (Transformer, GAN and Diffusion Models), and further proposes future development directions.
Image Forensics, Copy–Move Forgery Detection, Transformer, AIGC, Deepfake.
The Recent Digital Image Splicing Detection
With the great progress of multimedia, digital image tampering has become a critical challenge to modern social credit. Among these abundant tampering methods, image splicing is especially noteworthy. This paper is based on the search and organization of classical and recent papers, aiming to provide a taxonomy analysis on this topic. By classifying the image splicing detection technology into two main classes, four different sub-classes are proposed. Specifically, depending on whether artificial intelligence is applied, traditional methods and deep learning methods are divided from each other. A further classification of the traditional methods can be proposed based on the specific aspect of anomaly focused on by each method. Also, this paper is expected to provide a channel for researchers to quickly get to know the digital image splicing detection area. Besides, this paper finally hopes to provide a way to help researchers understand the status of this area and to conduct their technical assessments.
Image Tampering, Image Splicing, Pixel Anomaly, Photo Response Non-Uniformity (PRNU), Lighting Inconsistency.
Bypass Analysis Based on Convolutional Neural Networks
Side-channel analysis, as a key branch of cryptanalysis, poses a serious threat to the security of embedded smart devices. With the development of artificial intelligence technology, side-channel cryptanalysis methods based on convolutional neural networks (CNNSCA) have become a research hotspot. This paper focuses on the modeling and analysis of CNNSCA, exploring its application in side-channel cryptographic attacks. In traditional side-channel analysis techniques, non-modeling methods such as Differential Power Analysis (DPA) and Correlation Power Analysis (CPA) require domain knowledge to construct guess models; modeling methods like Template Attacks (TA) involve template construction and matching phases. CNNSCA leverages the advantages of deep learning to automatically extract data features. Experimental results demonstrate that CNNSCA outperforms traditional TA when attacking ideal low-noise side-channel signals, and shows greater advantage in attacking distorted side-channel signals due to noise and countermeasures, providing new avenues for improving the efficiency of side-channel cryptographic attacks and countering protective strategies, which is of significant importance for ensuring the security of cryptographic systems.
Convolutional Neural Networks, Side-Channel Analysis, VGGNet, AlexNet.
Alignment Methods for Large Language Models Based on Human Feedback
In recent years, artificial intelligence has developed rapidly, and large language models have been widely used. However, as the capabilities of the model continue to improve, there is a risk that its outputs may contain inaccurate information, be misleading, or deviate from human values. To ensure that the model is safe, reliable, and adheres to ethical standards, it is particularly crucial to guide the model's behavior using human feedback alignment techniques. This article systematically organizes relevant alignment methods, clarifies the core connotation of human feedback, further analyzes the key links in the alignment process, categorizes and discusses based on the principles and implementation technologies of the methods, organizes commonly used datasets and evaluation systems, and finally provides a summary. The significance of this research lies in constructing a complete knowledge system, clarifying the technical implementation path, and providing theoretical support and guidance for large language models to better align with human intentions and be safely applied in critical areas.
Human Feedback, Large Language Models, Alignment, Reinforcement Learning.
Face Forgery Detection Technology Based on Deep Learning
The rapid evolution of generative artificial intelligence has significantly transformed the way image and video content is produced. Deepfake facial recognition, a prominent example, has become a research hotspot in computer vision and multimedia security. However, its potential for widespread application also carries the risk of potential misuse, sparking continued attention from both academia and industry. This paper systematically reviews the face forgery detection technology based on deep learning. With the development of artificial intelligence technologies such as Generative Adversarial Networks (GAN) and diffusion models, face forgery content has brought about serious security and ethical issues while bringing application possibilities. Traditional image forensics methods rely on manual features and have limited generalization ability, while deep learning significantly improves detection performance through end-to-end feature learning. This paper reviews and analyzes the principles and characteristics of mainstream detection methods from the perspectives of physiological signal features, image tampering traces, GAN generation features and image-level learning strategies, and summarizes the challenges that the current models still face in terms of generalization ability and anti-interference ability. Finally, the future research directions such as cross-domain detection framework, multi-modal fusion and active defense are proposed.
Deep Learning, Face Forgery, Detection Technology.
Symmetric Encryption Framework for Reversible Data Hiding in the Encrypted Domain
In the context of the deep integration of digitalization and cloud computing, the demands for privacy protection and value-added services for multimedia data have become increasingly prominent. Reversible Data Hiding in Encrypted Images (RDHEI) technology, which can embed secret data into an encrypted carrier and losslessly recover the original carrier, has emerged as a key research direction in information security. As the core underpinning of RDHEI, the diversity and performance differences of symmetric encryption frameworks directly determine the upper limit of the technology's practical application. However, existing research has largely focused on optimizing single frameworks, lacking a systematic review of their principles, strategies, and complementarity. Conducting such a comprehensive study is of significant value for technology selection and future breakthroughs. This paper focuses on symmetric encryption frameworks in RDHEI, systematically reviewing three typical categories from four perspectives: technical principles, encryption mechanisms, embedding strategies, and performance complementarity. These frameworks include full bit-plane partitioning with pixel prediction, those based on the Binary Symmetric Channel (BSC) and polar codes, and 3D model octree subdivision with multiple Most Significant Bit (MSB) prediction. The research herein not only provides a clear reference for RDHEI technology selection but also points towards the future direction of dynamic framework fusion, offering a new path to overcome bottlenecks in the information security domain by balancing capacity, complexity, and reversibility through modular design.
Reversible Data Hiding in Encrypted Images (RDHEI), Symmetric encryption framework, Binary Symmetric Channel (BSC), Octree subdivision.
Theoretical Framework and Application Exploration of Fully Homomorphic Encryption
With the rapid development of big data and cloud computing, the issues of data privacy and security have gradually attracted widespread attention worldwide. Fully Homomorphic Encryption (FHE), as one of the key technologies for solving privacy computing problems, can perform addition and multiplication operations in the encrypted state, thereby enabling effective computation while ensuring data privacy. This paper reviews the theoretical framework and application exploration of FHE. Firstly, it comprehensively summarizes the theoretical basis, development history, and key mathematical tools of FHE, and focuses on analyzing the algorithm principles and latest progress of mainstream schemes such as Brakerski-Fan-Vercauteren (BFV), Cheon-Kim-Kim-Song (CKKS), and Torus FHE (TFHE). It explores the application potential of FHE in fields such as machine learning, medical health, and cloud computing, and analyzes the core challenges it faces, including performance bottlenecks, bootstrapping overhead, and multi-user management. Finally, it looks forward to future research directions such as the integration of FHE and AI, and the construction of cross-domain privacy computing platforms, emphasizing the significant importance of FHE in promoting the two-way advancement of data security and privacy protection.
Fully Homomorphic Encryption, Development History, Main Algorithms.
Image-level Face Forgery Detection Methods: Revolutions, Challenges and Future Look
The rapid development of deep forgery technology poses a serious challenge to social information security. This paper systematically reviews the image-level face forgery detection methods, focusing on two technical routes: the spatial domain and the frequency domain. Spatial-domain methods achieve detection by analyzing pixel-level texture, color consistency, and boundary artifacts, supplemented by deep learning models; while frequency-domain methods use spectral features (e.g., high-frequency mesh traces) to reveal anomalies left behind by the generative model to detect the authenticity of an image. The study shows that the spatial-domain approach based on deep learning is easy to understand and has excellent detection accuracy, but lacks generalization; the frequency-domain approach is robust to compression perturbations, but is difficult to adapt to new generation techniques (e.g., diffusion models). In the future, people need to break the bottleneck of generalization, integrate multimodal features and explore lightweight design to improve the evaluation system of detection accuracy.
Deepfake detection, Spatial domain characters, Frequency characters, Diffusion model, Generalizability.
Progress in Image Source Identification and Forensic Technology
Image source identification forensics, as a core branch of passive digital image forensics, aims to identify the source device or generation method through the implicit physical or algorithmic characteristics of the image itself, in order to deal with the authenticity threat posed by image forgery technology. This paper systematically sorts out the technical evolution path of image source identification forensics, compares and analyzes the technical characteristics and limitations of traditional model-based methods and deep learning-driven methods, and discusses the four major sub-directions of camera source identification, computer graphics-generated image forensics, AI-synthesized image forensics, and re-acquired image forensics. Based on the shortcomings of existing research, this paper proposes key research directions such as the need to integrate physical prior knowledge with deep learning models, build a cross-modal general framework, and enhance adversarial robustness and unknown source detection capabilities. It also predicts that multimodal feature fusion, self-supervised learning, and explainable modeling will become the focus of future technological breakthroughs.
traditional model approach, deep learning method, camera source recognition, AI synthetic image forensics, reacquired image detection.
Multimodal Emotion Recognition Empowers Fatigue Driving Detection
The probability of traffic accidents caused by fatigue is not small, so the reliable fatigue reminder device is an important way to ensure driving safety. Therefore, this paper introduces six kinds of multimodal fatigue driving detection techniques with research significance: (1) a method based on heart rate and Percentage of Eyelid Closure(PERCLOS); (2) a method based on hybrid electroencephalography(EEG) and eye tracking; (3) attention-based approach; (4) a method based on multimodal feature coupled mode; (5) a method based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture; (6) a non-invasive method based on heart rate, eye and facial features. Instead of simply listing the existing methods, this paper systematically analyzes the performance of these methods in terms of accuracy, robustness, hardware cost and practical deployment feasibility. The results show that the accuracy of most models is more than 96%. In addition, this paper also critically discusses the main limitations of current methods, and proposes corresponding improvement directions. Finally, this article discusses the application prospects of these technologies in the automotive and transportation fields and clarifies the advantages and disadvantages of these six technologies. This article provides a reference for designing low-cost and easy-to-use in vehicle fatigue driving detection solutions in the field of automotive, bus, aircraft and commercial vehicle fleet. It is helpful to promote the practical application of fatigue detection technology in vehicles and aircraft, thereby improving road safety and reducing the occurrence of fatigued driving accidents.
Fatigue Driving Detection, Multimodal Fusion, Attention Mechanism, Intelligent Transportation Systems.