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A 3D Memristive Cubic Map with Dual Discrete Memristors: Design, Implementation, and Application in Image Encryption

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Gao, Suo Iu, Herbert Ho-Ching Erkan, Ugur Simsek, Cemaleddin Toktas, Abdurrahim Cao, Yinghong Wu, Rui Mou, Jun Li, Qi Wang, Chunpeng Dalian Polytechnic University School of Information Science and Engineering Dalian116034 China The University of Western Australia School of Electrical Electronic and Computer Engineering Perth6009 Australia Faculty of Engineering Electrical and Electronics Engineering Respectively Karamanoglu Mehmetbey University Karaman70200 Turkey Ankara University Engineering Faculty Department of Artificial Intelligence and Data Engineering Ankara06830 Turkey Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

Discrete chaotic systems based on memristors exhibit excellent dynamical properties and are more straightforward to implement in hardware, making them highly suitable for generating cryptographic keystreams. However, most existing memristor-based chaotic systems rely on a single memristor. This paper introduces a novel discrete chaotic system employing dual memristors, named the 3D memristive cubic map with dual discrete memristors (3D-MCM). The 3D-MCM system demonstrates richer and more intricate dynamical behaviors compared to its single-memristor counterparts, as verified through bifurcation diagrams, Lyapunov exponent spectra, and complexity analyses. Notably, the system exhibits coexisting attractors, substantially enhancing its dynamical complexity. Hardware implementation of the 3D-MCM attractors confirms its feasibility for industrial applications. To illustrate the system's potential in encryption tasks, this study integrates the quaternary-based permutation and dynamic emanating diffusion (QPDED-IE) scheme for image encryption with the *** results demonstrate that the QPDED-IE scheme based on the 3D-MCM exhibits strong diffusion and confusion properties, effectively resisting cryptanalytic attacks. © 1991-2012 IEEE.

关键词： Bifurcation (mathematics)

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NUC-Net: Non-uniform Cylindrical Partition Network for Efficient LiDAR Semantic Segmentation

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Wang, Xuzhi Feng, Wei Kong, Lingdong Wan, Liang Tianjin Normal University College of Computer and Information Engineering Tianjin300380 China Tianjin Key Laboratory of Student Mental Health and Intelligence Assessment China Tianjin University College of Intelligence and Computing Tianjin300350 China National University of Singapore Department of Computer Science 117597 Singapore

LiDAR semantic segmentation plays a vital role in autonomous driving. Existing voxel-based methods for LiDAR semantic segmentation apply uniform partition to the 3D Li- DAR point cloud to form a structured representation based on cartesian/cylindrical coordinates. Although these methods show impressive performance, the drawback of existing voxel-based methods remains in two aspects: (1) it requires a large enough input voxel resolution, which brings a large amount of computation cost and memory consumption. (2) it does not well handle the unbalanced point distribution of LiDAR point cloud. In this paper, we propose a non-uniform cylindrical partition network named NUC-Net to tackle the above challenges. Specifically, we propose the Arithmetic Progression of Interval (API) method to non-uniformly partition the radial axis and generate the voxel representation which is representative and efficient. Moreover, we propose a non-uniform multi-scale aggregation method to improve contextual information. Our method achieves state-of-the-art performance on SemanticKITTI and nuScenes datasets with much faster speed and much less training time. And our method can be a general component for LiDAR semantic segmentation, which significantly improves both the accuracy and efficiency of the uniform counterpart by 4× training faster and 2× GPU memory reduction and 3× inference speedup. We further provide theoretical analysis towards understanding why NUC is effective and how point distribution affects performance. Code is available at https://***/alanWXZ/NUC-Net. © 1991-2012 IEEE.

关键词： Semantic Segmentation

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From Head to Tail: Efficient Black-box Model Inversion Attack via Long-tailed Learning

arXiv

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arXiv 2025年

作者： Li, Ziang Zhang, Hongguang Wang, Juan Chen, Meihui Hu, Hongxin Yi, Wenzhe Xu, Xiaoyang Yang, Mengda Ma, Chenjun Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University China Shanghai Innovation Institute China Ant Group China Department of Computer Science and Engineering University at Buffalo SUNY United States

Model Inversion Attacks (MIAs) aim to reconstruct private training data from models, leading to privacy leakage, particularly in facial recognition systems. Although many studies have enhanced the effectiveness of white-box MIAs, less attention has been paid to improving efficiency and utility under limited attacker capabilities. Existing black-box MIAs necessitate an impractical number of queries, incurring significant overhead. Therefore, we analyze the limitations of existing MIAs and introduce Surrogate Model-based Inversion with Long-tailed Enhancement (SMILE), a high-resolution oriented and query-efficient MIA for the black-box setting. We begin by analyzing the initialization of MIAs from a data distribution perspective and propose a long-tailed surrogate training method to obtain high-quality initial points. We then enhance the attack’s effectiveness by employing the gradient-free black-box optimization algorithm selected by NGOpt. Our experiments show that SMILE outperforms existing state-of-the-art black-box MIAs while requiring only about 5% of the query overhead. Our code is available at https://***/L1ziang/SMILE. Copyright © 2025, The Authors. All rights reserved.

关键词： Face recognition

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Provably Secure Efficient Key-Exchange Protocol for Intelligent Supply Line Surveillance in Smart Grids

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IEEE Internet of Things Journal 2025年

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Alenazi, Mohammed J.F. Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan 250353 China

Intelligent supply line surveillance is critical for modern smart grids. Smart sensors and gateway nodes are strategically deployed along supply lines to achieve intelligent surveillance. They collect data continuously and transmit it to the control centre in real-time. It enables real-time monitoring, fault detection, and efficient energy management across distribution networks. This advanced surveillance system ensures continuous monitoring of supply lines, detecting anomalies, and optimizing operations to maintain the stability and reliability of the smart grid. However, the reliance of all participating nodes on public communication channels to transmit supply line surveillance data exposes these systems to critical cyber attacks. These cyber attacks include impersonation, physical tampering, ephemeral secret leakage, and desynchronization attacks. To address these issues, existing key-exchange protocols often fail to ensure robust security while imposing high computation and communication overheads, limiting their practicality for resource-constrained environments. Therefore, we propose a secure and efficient key exchange and tamper-resistant authentication protocol using Elliptic Curve Cryptography (ECC) and Physical Unclonable Functions (PUFs). The PUF mechanism provides robust resistance against physical tampering and cloning attacks, ensuring enhanced physical security for supply line devices. We validate the security robustness of the devised protocol using the Random or Real (ROR) model. Furthermore, informal security analysis demonstrates the protocol's robustness in rigorously resisting various attacks, including physical tampering, impersonation, ephemeral secret leakage and desynchronization. Moreover, we determine the performance evaluation that reveals the protocol's superior efficiency compared to competing protocols, achieving significant reductions of 28.64% in computation overhead and 9.96% in communication overhead. © 2014 IEEE.

关键词： Authentication Protocol

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A transactional-behavior-based hierarchical gated network for credit card fraud detection

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IEEE/CAA Journal of Automatica Sinica 2025年

作者： Xie, Yu Zhou, MengChu Liu, Guanjun Wei, Lifei Zhu, Honghao De Meo, Pasquale College of Information Engineering Shanghai Maritime University Shanghai201306 China School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou310018 China Helen and John C. Hartmann Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Computer Science Tongji University Shanghai201804 China College of Computer Science and Information Engineering Bengbu University Bengbu233030 China Department of Ancient and Modern Civilizations University of Messina Messina98166 Italy

The task of detecting fraud in credit card transactions is crucial to ensure the security and stability of a financial system, as well as to enforce customer confidence in digital payment systems. Historically, credit card companies have used rule-based approaches to detect fraudulent transactions, but these have proven inadequate due to the complexity of fraud strategies and have been replaced by much more powerful solutions based on machine learning or deep learning algorithms. Despite significant progress, the current approaches to fraud detection suffer from a number of limitations: for example, it is unclear whether some transaction features are more effective than others in discriminating fraudulent transactions, and they often neglect possible correlations among transactions, even though they could reveal illicit behaviour. In this paper, we propose a novel credit card fraud detection (CCFD) method based on a transaction behaviour-based hierarchical gated network. First, we introduce a feature-oriented extraction module capable of identifying key features from original transactions, and such analysis is effective in revealing the behavioural characteristics of fraudsters. Second, we design a transaction-oriented extraction module capable of capturing the correlation between users' historical and current transactional behaviour. Such information is crucial for revealing users' sequential behaviour patterns. Our approach, called transactional-behaviour-based hierarchical gated network model (TbHGN), extracts two types of new transactional features, which are then combined in a feature interaction module to learn the final transactional representations used for CCFD. We have conducted extensive experiments on a real-world credit card transaction dataset with an increase in average F1 between 1.42% and 6.53% and an improvement in average AUC between 0.63% and 2.78% over the state of the art. © 2025 Institute of Electrical and Electronics Engineers Inc.. All right

关键词： Deep learning

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FAMOS: robust privacy-preserving authentication on payment apps via federated multi-modal contrastive learning 24

FAMOS: robust privacy-preserving authentication on payment a...

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Proceedings of the 33rd USENIX Conference on Security Symposium

作者： Yifeng Cai Ziqi Zhang Jiaping Gui Bingyan Liu Xiaoke Zhao Ruoyu Li Zhe Li Ding Li Key Laboratory of High Confidence Software Technologies (PKU) Ministry of Education and School of Computer Science Peking University Department of Computer Science University of Illinois Urbana-Champaign School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University School of Computer Science Beijing University of Posts and Telecommunications Ant Group

ISBN: (纸本)9781939133441

The rise of mobile payment apps necessitates robust user authentication to ensure legitimate user access. Traditional methods, like passwords and biometrics, are vulnerable once a device is compromised. To overcome these limitations, modern solutions utilize sensor data to achieve user-agnostic and scalable behavioral authentication. However, existing solutions face two problems when deployed to real-world applications. First, it is not robust to noisy background activities. Second, it faces the risks of privacy leakage as it relies on centralized training with users' sensor *** this paper, we introduce FAMOS, a novel authentication framework based on federated multi-modal contrastive learning. The intuition of FAMOS is to fuse multi-modal sensor data and cluster the representation of one user's data by the action category so that we can eliminate the influence of background noise and guarantee the user's privacy. Furthermore, we incorporate FAMOS with federated learning to enhance performance while protecting users' privacy. We comprehensively evaluate FAMOS using real-world datasets and devices. Experimental results show that FAMOS is efficient and accurate for real-world deployment. FAMOS has an F1-Score of 0.91 and an AUC of 0.97, which are 42.19% and 27.63% higher than the baselines, respectively.

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FundusGAN: A Hierarchical Feature-Aware Generative Framework for high-Fidelity Fundus Image Generation

arXiv

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arXiv 2025年

作者： Hou, Qingshan Wang, Meng Cao, Peng Ke, Zou Liu, Xiaoli Fu, Huazhu Zaiane, Osmar R. Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China Alberta Machine Intelligence Institute University of Alberta Edmonton Canada Ophthalmology Yong Loo Lin School of Medicine National University of Singapore Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore

Recent advancements in ophthalmology foundation models such as RetFound have demonstrated remarkable diagnostic capabilities but require massive datasets for effective pre-training, creating significant barriers for development and deployment. To address this critical challenge, we propose FundusGAN, a novel hierarchical feature-aware generative framework specifically designed for high-fidelity fundus image synthesis. Our approach leverages a Feature Pyramid Network within its encoder to comprehensively extract multi-scale information, capturing both large anatomical structures and subtle pathological features. The framework incorporates a modified StyleGAN-based generator with dilated convolutions and strategic upsampling adjustments to preserve critical retinal structures while enhancing pathological detail representation. Comprehensive evaluations on the DDR, DRIVE, and IDRiD datasets demonstrate that FundusGAN consistently outperforms state-of-the-art methods across multiple metrics (SSIM: 0.8863, FID: 54.2, KID: 0.0436 on DDR). Furthermore, disease classification experiments reveal that augmenting training data with FundusGAN-generated images significantly improves diagnostic accuracy across multiple CNN architectures (up to 6.49% improvement with ResNet50). These results establish FundusGAN as a valuable foundation model component that effectively addresses data scarcity challenges in ophthalmological AI research, enabling more robust and generalizable diagnostic systems while reducing dependency on large-scale clinical data collection. Copyright © 2025, The Authors. All rights reserved.

关键词： Ophthalmology

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Staging of Different Tumor by Utilizing Laser Guidance (@405 nm) in CT Scan Images Along with Statistical Analysis

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Lasers in engineering 2025年第4-6期59卷 309-325页

作者： Amjad, M. Ullah, H. Siddique, M.A. Majid, M.A. Aleem, A. Akhtar, M. Gilanie, G. Abdullah-Al-Wadud, M. Farhan, M. Akram, A. Batool, Z. Nazir, A. Biophotonics Imaging Techniques Laboratory Institute of Physics The Islamia University of Bahawalpur 63100 Pakistan Department of Computer Science Faculty of Computing The Islamia University of Bahawalpur Bahawalpur Pakistan Dept. of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia School of Earth Sciences and Engineering Hohai University Nanjing China

Early detection of lung cancer, liver cancer (HCC), and pancreatitis is crucial for effective treatment. This study assessed the use of laser guidance CT scans for diagnosis and staging. 26 patients of CT scan which were collected from BVH and civil hospital Bahawalpur, Pakistan. Visible light with a 405 nm wavelength is used. Results showed staging of lung cancer (2 at stage 1, 2 at stage 2, 3 at stage 4) and HCC (stages 1 to 4a). Pancreatic diseases detected included acute pancreatitis (44%), chronic pancreatitis (22%), gallstones (22%), and pancreatic tumors (11%). Alkaline phosphatase, SGOT, and blood urea levels were notably elevated in all three cancers compared to controls. CT scans show promise in identifying cancer stages and diagnosing pancreatitis, aiding in tailored treatment. Statistical analysis shows that alkaline phosphatase emerges as a potential biomarker for these conditions. ©2025 Old City Publishing, Inc.

关键词： Image analysis

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An Imperceptible Adversarial Attack Against 3D Object Detectors in Autonomous Driving

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IEEE Internet of Things Journal 2025年

作者： Wang, Yizhou Wu, Libing Jin, Jiong Wang, Enshu Zhang, Zhuangzhuang Zhao, Yu Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Swinburne University of Technology School of Engineering MelbourneVIC3122 Australia Wuhan University School of Cyber Science and Engineering Wuhan430072 China City University of Hong Kong Department of Computer Science Hong Kong Hong Kong

As LiDAR-based 3D object detection gains attention, existing research on point cloud adversarial attacks has exposed vulnerabilities in 3D neural network models, which can further impact the reliability of perception systems in autonomous driving. However, current adversarial attacks primarily focus on the point cloud classification tasks, while detection tasks are more challenging to attack because they involve the localization of multiple targets and the sparse distribution of objects. Existing methods often implement attacks by adding global perturbations to the point clouds, which results in poor attack performance and lack of imperceptibility. In this paper, we investigate the robustness of 3D object detectors against adversarial examples. We propose a novel imperceptible attack method to generate 3D adversarial point clouds. First, we introduce the saliency map for the point clouds, assigning the unique value to each point to measure its importance to the model's discrimination results. These salient points are then aggregated and adaptively matched to different attack areas corresponding to different targets. Next, we design an optimization-based attack algorithm to generate adversarial point clouds, which are supervised by a dual-loss function consisting of the detection loss to ensure attack effectiveness and the distance loss to limit gap with the original point cloud. Finally, we conducted experiments on two real-world datasets, the KITTI and Waymo Open datasets, to evaluate the proposed attack method. Extensive experiments demonstrate that our attack method achieves average attack success rates of 83.07% and 77.11% on two datasets against seven 3D object detectors with minimal perturbations. Additionally, the generated adversarial point clouds exhibit strong transferability across multiple mainstream detectors. © 2025 IEEE.

关键词： Object detection

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A Robust Key Exchange and Tamper-Resistant Protocol for HAN and NAN Networks in Smart Grids

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IEEE Internet of Things Journal 2025年

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Shamshad, Salman Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China

Smart Grids (SG) rely on Home Area Networks (HAN) and Neighborhood Area Networks (NAN) to ensure efficient power distribution, real-time monitoring, and seamless communication between smart devices. Despite these advantages, the use of public communication channels in HAN and NAN networks introduces critical challenges, such as vulnerability to impersonation, physical tampering, and scalability issues in resource-constrained environments. These issues compromise the stability, reliability, and security of SG environments. Existing protocols often fail to adequately address these challenges, particularly in ensuring resistance to physical tampering of supply lines and impersonation attacks. Additionally, they struggle to minimize the computation, communication, and energy costs associated with securing a resource-constrained SG environment. Therefore, we propose a robust key exchange and tamper-resistant protocol for HAN and NAN networks to address these limitations. The proposed protocol leverages the Physical Unclonable Function (PUF) mechanism to offer physical tampering resistance to smart meters. We validate our protocol formally using the Random or Real (RoR) model, which confirms its security robustness. Additionally, our informal security analysis highlights the protocol's resilience against impersonation, physical tampering attacks, etc. We analyze and compare the performance of the proposed protocol, which further demonstrates our protocol's effectiveness and security compared to competing protocols. Moreover, the proposed protocol achieves resource efficiency with 45.99% and 58.85% substantial reductions in computation overhead and energy overhead, respectively, compared to competing protocols. These results showcase the protocol's enhanced security and practicality for resource-constrained SG environments. © 2014 IEEE.

关键词： Authentication Protocol

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