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34th ACM Web Conference, WWW 2025

作者： Yang, Jiyu He, Qiang Zhou, Zheyu Dai, Xiaohai Chen, Feifei Tian, Cong Yang, Yun Swinburne University of Technology Melbourne Australia Huazhong University of Science and Technology Wuhan China Deakin University Melbourne Australia Xidian University Xi’an China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9798400712746

Machine learning (ML) models are widely deployed on edge nodes, such as mobile phones and edge servers, to power a wide range of AI applications over the web. Ensuring the integrity of these edge models is paramount, as they are subject to corruption caused by software/hardware exceptions and malicious tampering, which may undermine model performance, incur economic losses, and pose health risks. Existing data integrity mechanisms designed for files stored on disks cannot properly verify the integrity of models running in GPUs or mitigate the new integrity threats against edge models. This paper proposes EdgeThemis, a novel mechanism for verifying the integrity of edge models through sentinel verification. To enable verifiability for a model M, EdgeThemis embeds a sentinel backdoor and a verification module into M. Then, a challenger can send verification requests to the edge node hosting M to verify its integrity. Next, the sentinel activates the verification module to generate a unique integrity proof tied to the identity of the edge node for verification. Finally, the challenger can verify the integrity proof to detect model corruption. Theoretical analysis proves that EdgeThemis can properly mitigate potential integrity threats against edge models. Experiments demonstrate that EdgeThemis achieves a verification accuracy of 100.00% across various models and different types of model corruption with robustness against replay attacks, theft attacks, and replacement attacks. © 2025 Copyright held by the owner/author(s).

关键词： Backpropagation

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Deepfake Detection and Mitigation Using Advanced CNN: Ensuring Digital Content Integrity

Deepfake Detection and Mitigation Using Advanced CNN: Ensuri...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Palvi Sharma Shubham Gupta Monu Sharma Neha Gupta Dept. of CSE Model Institute of Engineering & Technology Jammu India Valley Health System Winchester Virginia School of Computer Science and Engineering Geeta University

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Deepfake technology, with its hyper-realistic fake content and backed by AI technologies, presents a serious threat to the authenticity and security of digital content. The study deployed three different machine learning-based models: Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Vision Transformers (ViT) for discrimination of real and fake faces. The model leverages the capabilities of CNN for automatic spatial feature learning from raw data, giving it exceptional performance in the discrimination between real and fake content. Performance measures were made in terms of accuracy, precision, recall, and F1 score and compared against traditional methods; the model remains a trustworthy contribution towards deepfake detection. Findings indicate that the CNN exceeded others with the highest precisions of up to 92.21%, recall of 90%, F1 score of 93%, and perfect accuracy of 97.25%. The paper further discusses the future developments, for instance, architectural improvements and real-time applications and expanding detection capacity outside visual data for a more formidable approach towards new deepfake formats, such as audio and text. This endeavor lays the groundwork for viable and scalable roads against the rising onslaught on digital media manipulation, ensuring content integrity in an AI-driven world.

关键词： Support vector machines Representation learning Deepfakes Visualization Accuracy Roads Transformers Convolutional neural networks Security Artificial intelligence

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Small Signal Stability-Guaranteed Moving Target Defense Against Load Redistribution Attack On IoT-based Smart Grid

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IEEE Internet of Things Journal 2025年第11期12卷 17413-17429页

作者： Wang, Bingdong Zhang, Zhenyong Wang, Mufeng Liu, Mengxiang Ye, Rui Deng, Ruilong Zhang, Xin Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China University of Sheffield Department of Automatic Control and System Engineering Sheffield United Kingdom Guizhou Power Grid Co. Ltd. Intelligent Operation Center China Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China Huzhou Institute of Industrial Control Technology Huzhou313000 China

Moving target defense (MTD) is a promising approach to defend against load redistribution attacks on the internet-of-things (IoT)-based smart grid networks by probing the distorted state estimates with the distributed flexible AC transmission system. However, existing studies mainly focus on optimizing the performance of MTD and ignore the safety effect of it on the system’s operation. In this paper, we fill this gap by deeply analyzing the effect of MTD on the small signal stability and aim to alleviate the negative impact and guarantee its defending performance simultaneously. Firstly, the stability is formally described using the eigenvalue sensitivity. The relationship between the MTD-induced perturbation (MTDper) and the stability criteria is derived. Secondly, a new indicator is proposed to measure the effectiveness of MTDper. Thirdly, a constrained optimization problem is formulated to compute the bound of MTDper for guaranteeing the small signal stability. In addition, a surprising finding is that the stability margin can be improved and enhanced by optimizing the value of MTDper without losing the MTD’s effectiveness. Finally, we evaluate the performance of MTDper and its impact on the small signal stability with extensive simulations on the IEEE 30-bus, 39-bus, and 68-bus test power systems. © 2014 IEEE.

关键词： Constrained optimization

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Port-controllable routing of orbital angular momentum modes using a rotatable diffractive neural network

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science China(Physics,Mechanics & Astronomy) 2025年第6期Mechanics & Astronomy) .卷 158-167页

作者： Junmin Liu Jiafu Chen Qingji Zeng Zemin Liang Xinping Wu Xin Zhao Jiangnan Xiao Huapeng Ye Ze Dong Dianyuan Fan Shuqing Chen Institute of Microscale Optoelectronics Shenzhen University College of New Materials and New Energies Shenzhen Technology University Terahertz Technology Innovation Research Institute Shanghai Key Laboratory of Modern Optical System Terahertz Science Cooperative Innovation Center School of Optical-Electrical Computer Engineering University of Shanghai for Science and Technology Guangdong Provincial Key Laboratory of Optical Information Materials and Technology & Institute of Electronic Paper Displays South China Academy of Advanced Optoelectronics South China Normal University School of Information and Electronics Beijing Institute of Technology

Orbital angular momentum(OAM) modes provide an additional orthogonal physical dimension, offering transformative potential for enhancing optical communication capacity. Despite significant progress in mode multiplexing, the development of robust communication networks faces persistent challenges, particularly in effectively routing and controlling these multiplexed channels among network nodes. To tackle these dilemmas, we propose a rotatable diffractive neural network(R-DNN) strategy and demonstrate its capability for port-controllable OAM mode routing. By leveraging the correlation between the orthogonal evolution of OAM modes in free space and phase modulations during propagation, the R-DNN precisely shapes the spatial evolution of mode fields through multiple rotatable phase layers, enabling efficient routing to specific output ports. This approach exploits the interaction of secondary wavelets with the relative states of the rotatable layers, allowing on-demand control of mode evolution paths and enhancing routing flexibility. As a proof of concept, we developed a tri-functional router that successfully directs three OAM modes to individually controllable output ports. This router achieves an average intermode crosstalk of less than-16.4 dB across three functional states, one-dimensional, two-dimensional, and cross-connected switching, while supporting the routing of 5.85 Tbit/s quadrature phase-shift keying signals. These results highlight the R-DNN's effectiveness in achieving precise and controllable OAM mode manipulation, paving the way for advanced applications in mode-multiplexed communication networks and beyond.

关键词： orbital angular momentum mode mode routing diffractive neural network

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FDDSGCN: Fractional Decoupling Dynamic Spatiotemporal Graph Convolutional Network for Traffic Forecasting

FDDSGCN: Fractional Decoupling Dynamic Spatiotemporal Graph ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jinpeng Xu Chunna Zhao Jing Yang Yaqun Huang Yaoyuan Yang Por Lip Yee School of Information Science & Engineering Yunnan University Kunming China Department of Computer System and Technology Universiti Malaya Kuala Lumpur Malaysia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Urban traffic flow management faces increasing challenges due to accelerating urbanization. Traffic data collected from roadside sensors contain complex temporal and spatial dependencies that interact simultaneously. Although Graph Neural Networks and Recurrent Neural Networks have been successful in capturing these dependencies, two critical issues remain: 1) Treating all traffic signals equally fails to capture the nuanced spatiotemporal dependencies hidden in time series data; 2) Dynamic traffic conditions hinder the accurate capture of local spatial dependencies, thereby limiting prediction accuracy and reliability. To address these challenges, we propose an innovative model, FDDSGCN. To resolve the first issue, we introduce Fractional Residual Decomposition, which effectively separates traffic data into spatial and temporal signals. For the second issue, we employ Dynamic Spatial-Temporal Graph Convolution with fractional-order weight adjustments to dynamically capture local dependencies. Additionally, the Long Short-Term Dependency module analyzes both long-term and short-term dependencies. Extensive experiments on three public datasets demonstrate the superior performance and practical value of our model.

关键词： Accuracy Convolution Urban areas Time series analysis Spatial databases Spatiotemporal phenomena Sensors Speech processing Spatial resolution Signal resolution

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Covert Communications in Active-IOS Aided Uplink NOMA systems With Full-Duplex Receiver

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8419-8424页

作者： Kang, Xueyu Qi, Nan Lv, Lu Boulogeorgos, Alexandros-Apostolos A. Tsiftsis, Theodoros A. Liu, Hongwu Shandong Jiaotong University School of Information Science and Electrical Engineering Jinan250357 China Nanjing University of Aeronautics and Astronautics Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Nanjing210016 China Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China Xidian University School of Telecommunications Engineering Xi'an710071 China University of Western Macedonia Department of Electrical and Computer Engineering Kozani50100 Greece University of Thessaly Department of Informatics and Telecommunications Lamia35100 Greece University of Nottingham Ningbo China Department of Electrical and Electronic Engineering Ningbo315100 China

In this paper, an active intelligent omni-surface (A-IOS) is deployed to aid uplink transmissions in a non-orthogonal multiple access (NOMA) system. In order to shelter the covert signal embedded in the superposition transmissions, a multi-antenna full-duplex (FD) receiver is utilized at the base-station to recover signal in addition to jamming the warden. With the aim of maximizing the covert rate, the FD transmit and receive beamforming, A-IOS refraction and reflection beamforming, NOMA transmit power, and FD jamming power are jointly optimized. To tackle the non-convex covert rate maximization problem subject to the highly coupled system parameters, an alternating optimization algorithm is designed to iteratively solve the decoupled sub-problems of optimizing the system parameters. The optimal solutions for the sub-problems of the NOMA transmit power and FD jamming power optimizations are derived in closed-form. To tackle the rank-one constrained non-convex fractional programming of the A-IOS beamforming and FD beamforming, a penalized Dinkelbach transformation approach is proposed to resort to the optimal solutions via semidefinite programming. Numerical results clarify that the deployment of the A-IOS significantly improves the covert rate compared with the passive-IOS aided uplink NOMA system. It is also found that the proposed scheme provides better covert communication performance with the optimized NOMA transmit power and FD jamming power compared with the benchmark schemes. © 1967-2012 IEEE.

关键词： Jamming

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5G Beyond for Healthcare: Leveraging AI/ML and Diverse Datasets for Cybersecurity 2nd

5G Beyond for Healthcare: Leveraging AI/ML and Diverse Dat...

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2nd International Workshop on Secure and Resilient Digital Transformation of Healthcare, SUNRISE 2024

作者： Sodhro, Ali Hassan Mughal, Muhammad Irfan Younas Iqbal, Muhammad Javed Department of Computer Science Kristianstad University Kristianstad29188 Sweden Computer System Engineering Sukkur IBA University Sukkur65200 Pakistan Institute of Space Science and Technology University of Karachi Karachi75270 Pakistan

ISBN: (纸本)9783031855573

The rapid adoption of 5G networks, space networks, and Internet of Things (IoT) technologies in healthcare has significantly expanded the attack surface for cybersecurity threats. This evolving landscape demands robust defense mechanisms that can anticipate and neutralize sophisticated cyber-attacks. Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) technologies are pivotal in developing such advanced cybersecurity solutions. A critical factor influencing the effectiveness of these AI/ML models is the quality and diversity of the datasets used in their training. This paper presents a systematic review of various datasets used for AI/ML-based cybersecurity model training across multiple domains, with a focus on 5G networks, IoT healthcare, and space networks. By employing a structured Goal-Question-Metric (GQM) methodology and Quasi-Gold Standard (QGS) validation, we assessed the characteristics, applications, and limitations of real, synthetic, and hybrid datasets in enhancing cybersecurity measures. The review identifies key trends, gaps, and future research directions, highlighting the need for more diverse datasets, standardized benchmarks, and privacy-preserving techniques. Our findings offer insights into improving the resilience of AI/ML models for cybersecurity, guiding the development of more effective and adaptable defense strategies across emerging network technologies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： 5G mobile communication systems

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A Framework for Deepfake Detection using Convolutional Neural Network and Deep Features

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Procedia computer science 2025年 258卷 3640-3648页

作者： Soundarya B C Gururaj H L Naveen Kumar C M Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Business System Malnad College of Engineering Hassan India

With the advancement of Artificial Intelligence, facial recognition has become a crucial biometric feature. Deepfake technology leverages AI and can create hyper-realistic digitally manipulated videos of people appearing to say or do things that never occurred. The emergence of Generative Adversarial Networks (GANs) has further enabled the creation of fake visual content with astonishing realism. This technology has diverse applications, such as in the film industry, where it allows for video recreation without reshooting, creating awareness videos, restoring the voices of those who have lost them, and updating movie scenes at low cost. However, this rapid advancement also presents significant challenges. The proliferation of synthetic images raises severe concerns about their societal impact, particularly in terms of potential misuse for harassment and blackmail. Therefore, developing robust deepfake detection models is imperative. This study evaluates the performance of a proposed ResNet34 model in deepfake detection. We utilize the FaceForensics++ dataset to train and assess the model, incorporating images generated by four popular deepfake techniques. Our experimental results demonstrate that integrating linear ternary patterns (LTP) and edge detection-based features with the modified ResNet34 model achieves superior performance, attaining 97.5% accuracy and surpassing other approaches.

关键词： Deepfake Artificial intelligence Deep learning

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Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles 59

Generative AI-Enhanced Cooperative MEC of UAVs and Ground St...

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59th Annual Conference on Information sciences and systems, CISS 2025

作者： You, Jiahao Jia, Ziye Dong, Chao Wu, Qihui Han, Zhu Nanjing University of Aeronautics and Astronautics The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing211106 China Southeast University National Mobile Communications Research Laboratory Nanjing211111 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

ISBN: (纸本)9798331513269

The increasing deployment of unmanned surface vehicles (USVs) require computational support and coverage in applications such as maritime search and rescue. Unmanned aerial vehicles (UAVs) can offer low-cost, flexible aerial services, and ground stations (GSs) can provide powerful supports, which can cooperate to help the USVs in complex scenarios. However, the collaboration between UAVs and GSs for USVs faces challenges of task uncertainties, USVs trajectory uncertainties, heterogeneities, and limited computational resources. To address these issues, we propose a cooperative UAV and GS based robust multi-access edge computing framework to assist USVs in completing computational tasks. Specifically, we formulate the optimization problem of joint task offloading and UAV trajectory to minimize the total execution time, which is in the form of mixed integer nonlinear programming and NP-hard to tackle. Therefore, we propose the algorithm of generative artificial intelligence-enhanced heterogeneous agent proximal policy optimization (GAI-HAPPO). The proposed algorithm integrates GAI models to enhance the actor network ability to model complex environments and extract high-level features, thereby allowing the algorithm to predict uncertainties and adapt to dynamic conditions. Additionally, GAI stabilizes the critic network, addressing the instability of multi-agent reinforcement learning approaches. Finally, extensive simulations demonstrate that the proposed algorithm outperforms the existing benchmark methods, thus highlighting the potentials in tackling intricate, cross-domain issues in the considered scenarios. © 2025 IEEE.

关键词： Unmanned surface vehicles

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Compressed VGGNet for Automatic COVID-19 Disease Detection from CT Scan Images 8th

Compressed VGGNet for Automatic COVID-19 Disease Detection f...

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8th IFIP TC 12 International Conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Vinothini, R. Niranjana, G. Dhamanti, Inge Lailiyah, Syifa’ul Yakub, Fitri Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur 603203 India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur 603203 India Department of Health Policy and Administration Faculty of Public Health Universitas Airlangga Surabaya Indonesia Faculty of Health Medicine and Life Sciences Universitas Airlangga East Java Indonesia Electronic System Engineering Department Malaysia-Japan International Institute of Technology Kuala Lumpur Malaysia

ISBN: (纸本)9783031736162

Millions of people across the globe were affected by this rapidly spreading disease by the year 2020. Contamination of the respiratory system results from contracting COVID-19. Nonetheless, establishing a conclusive diagnosis of COVID-19 may prove difficult due to the subtle differences it shares with conventional pneumonia and the complexities involved in identifying areas of infection. A growing number of approaches based on deep learning (DL) are being proposed for the automated detection of COVID-19 from CT scan images. By employing the data pretreatment methodology, unprocessed image data becomes prepared for further analysis. The proposed framework utilised transfer learning to construct VGGNet, which is capable of discerning the Covid-19 disease. The model was subsequently assessed in comparison to the most sophisticated models, VGG16 and VGG19, using the SARS-COV2 CT scan dataset. This dataset contains 1230 CT scans of non-infected images and 1252 CT scans of Covid-19-infected images. The model has achieved 99% accuracy, 98% precision, 97% recall, and 98% F1-score, among other performance metrics. © IFIP International Federation for Information Processing 2025.

关键词： COVID-19

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