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Reversible Data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

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IEEE Internet of Things Journal 2025年

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： adaptive coding chinese remainder theorem for polynomials encrypted images Reversible data hiding secret sharing

Provably Convergent Federated Trilevel Learning

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arXiv 2023年

作者： Jiao, Yang Yang, Kai Wu, Tiancheng Jian, Chengtao Huang, Jianwei Department of Computer Science and Technology Tongji University China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University China Shanghai Research Institute for Intelligent Autonomous Systems China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China

Trilevel learning, also called trilevel optimization (TLO), has been recognized as a powerful modelling tool for hierarchical decision process and widely applied in many machine learning applications, such as robust neural architecture search, hyperparameter optimization, and domain adaptation. Tackling TLO problems has presented a great challenge due to their nested decision-making structure. In addition, existing works on TLO face the following key challenges: 1) they all focus on the non-distributed setting, which may lead to privacy breach;2) they do not offer any non-asymptotic convergence analysis which characterizes how fast an algorithm converges. To address the aforementioned challenges, this paper proposes an asynchronous federated trilevel optimization method to solve TLO problems. The proposed method utilizes µ-cuts to construct a hyper-polyhedral approximation for the TLO problem and solve it in an asynchronous manner. We demonstrate that the proposed µ-cuts are applicable to not only convex functions but also a wide range of non-convex functions that meet the µ-weakly convex assumption. Furthermore, we theoretically analyze the non-asymptotic convergence rate for the proposed method by showing its iteration complexity to obtain ϵ-stationary point is upper bounded by (Formula Presented). Extensive experiments on real-world datasets have been conducted to elucidate the superiority of the proposed method, e.g., it has a faster convergence rate with a maximum acceleration of approximately 80%. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

PMU-Spill: Performance Monitor Unit Counters Leak Secrets in Transient Executions

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PMU-Spill: Performance Monitor Unit Counters Leak Secrets in...

IEEE Asian Hardware-Oriented Security and Trust (AsianHOST)

作者： Pengfei Qiu Qiang Gao Dongsheng Wang Yongqiang Lyu Chang Liu Xiaoyong Li Chunlu Wang Gang Qu Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Tsinghua University Beijing China Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China Ministry of Education Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Beijing China Department of Electrical and Computer Engineering Institute for Systems Research University of Maryland Maryland USA

ISBN: (纸本)9781665461153

The processor's Performance Monitor Unit (PMU) allows the recording of architectural and microarchitectural events for profiling purposes. In this study, we reveal a security issue caused by the fact that current PMU implementations are capable of recording some events that happened during transient executions. We propose the PMU -Spill attack, a new kind of attack that enables attackers to maliciously leak the secret data in transient executions. We demonstrate on real hardware that PMU -Spill attack can leak the secret data stored in Intel Software Guard Extensions (SGX). In addition, we perform a thorough study to reveal all the vulnerable PMU counters and find that 20 of them can be used to achieve PMU -Spill attack. Our experiments suggest that the throughput of PMU -Spill attack is up to 575.3 bytes per second (Bps) with an average error rate of 1.89% when leaking the SGX-protected secret data.

关键词： Side-channel attacks Throughput Phasor measurement units Hardware Software Recording Security

Impact of Foreign Objects on Wireless Power Transfer Efficiency in Magnetic Coupling systems

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Impact of Foreign Objects on Wireless Power Transfer Efficie...

Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), International Conference on

作者： Hassan Khalid Saad Mekhilef Marizan Mubin Mehdi Seyedmahmoudian Mehmet Zahid Erel Hussain Bassi Obaid Alshammari Department of Electrical Engineering Power Electronics and Renewable Energy Research Laboratory (PEARL) University of Malaya Kuala Lumpur Malaysia Department of Electrical Engineering School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne Victoria Australia Department of Electrical Engineering University of Malaya Kuala Lumpur Malaysia School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne Victoria Australia Department of Energy Systems Engineering Ankara Yildirim Beyazit University Ankara Turkey Water Technologies Innovation Institute & Research Advancement (WTIIRA) Saudi Water Authority (SWA) Al Jubail Saudi Arabia Department of Electrical Engineering College of Engineering University of Hail Hail Saudi Arabia

ISBN: (数字)9798350391183

ISBN: (纸本)9798350391190

Inductive wireless charging has emerged as a viable solution for charging electric vehicles (EVs) both in stationary mode and on the go, i.e., dynamic charging. EVs are produced at a commercial level, and their wired charging stations are widely available. However, challenges still restrict the development of wireless charging infrastructure on a commercial scale. One significant challenge is the presence of foreign objects on the magnetic coupler surface. Although several foreign object detection (FOD) methods exist, the implementation of each method is cost-dependent. Among these methods, the parametric detection method is considered the most cost-effective. However, the effective precision of this method is still debatable. In this article, the performance of the magnetic coupler is analyzed under the influence of different materials as foreign objects. The analysis presented in this article is threefold. Firstly, a mathematical model of the magnetic coupler in the presence of a foreign object is developed. Secondly, the effect of different materials is analyzed as a function of coil parameters, including inductance, resistance, and coil losses. Lastly, the magnetic field distribution and magnitude are studied using finite element analysis (FEA). The influence of these materials is ultimately evaluated in terms of output power losses and efficiency. The study concludes that the presence of aluminium as a foreign object is responsible for significant power losses in the wireless power transfer system.

关键词： Coils Magnetic flux Inductive charging Couplers Wireless power transfer Voltage Electric vehicles Mathematical models Magnetic analysis Magnetic fields

Frequency-mixed Single-source Domain Generalization for Medical Image Segmentation

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arXiv 2023年

作者： Li, Heng Li, Haojin Zhao, Wei Fu, Huazhu Su, Xiuyun Hu, Yan Liu, Jiang Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Medical Intelligence and Innovation Academy Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Hospital Shenzhen China Institute of High Performance Computing Agency for Science Technology and Research Singapore Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

The annotation scarcity of medical image segmentation poses challenges in collecting sufficient training data for deep learning models. Specifically, models trained on limited data may not generalize well to other unseen data domains, resulting in a domain shift issue. Consequently, domain generalization (DG) is developed to boost the performance of segmentation models on unseen domains. However, the DG setup requires multiple source domains, which impedes the efficient deployment of segmentation algorithms in clinical scenarios. To address this challenge and improve the segmentation model's generalizability, we propose a novel approach called the Frequency-mixed Single-source Domain Generalization method (FreeSDG). By analyzing the frequency's effect on domain discrepancy, FreeSDG leverages a mixed frequency spectrum to augment the single-source domain. Additionally, self-supervision is constructed in the domain augmentation to learn robust context-aware representations for the segmentation task. Experimental results on five datasets of three modalities demonstrate the effectiveness of the proposed algorithm. FreeSDG outperforms state-of-the-art methods and significantly improves the segmentation model's generalizability. Therefore, FreeSDG provides a promising solution for enhancing the generalization of medical image segmentation models, especially when annotated data is scarce. The code is available at https://***/liamheng/Non-IID Medical Image Segmentation. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

Pyramid Pixel Context Adaption Network for Medical Image Classification with Supervised Contrastive Learning

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arXiv 2023年

作者： Zhang, Xiaoqing Xiao, Zunjie Wu, Xiao Chen, Yanlin Zhao, Jilu Hu, Yan Liu, Jiang Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen518055 China Singapore Eye Research Institute 169856 Singapore

Spatial attention mechanism has been widely incorporated into deep neural networks (DNNs), significantly lifting the performance in computer vision tasks via long-range dependency modeling. However, it may perform poorly in medical image analysis. Unfortunately, existing efforts are often unaware that long-range dependency modeling has limitations in highlighting subtle lesion regions. To overcome this limitation, we propose a practical yet lightweight architectural unit, Pyramid Pixel Context Adaption (PPCA) module, which exploits multiscale pixel context information to recalibrate pixel position in a pixel-independent manner dynamically. PPCA first applies a well-designed cross-channel pyramid pooling to aggregate multiscale pixel context information, then eliminates the inconsistency among them by the well-designed pixel normalization, and finally estimates per pixel attention weight via a pixel context integration. By embedding PPCA into a DNN with negligible overhead, the PPCANet is developed for medical image classification. In addition, we introduce supervised contrastive learning to enhance feature representation by exploiting the potential of label information via supervised contrastive loss. The extensive experiments on six medical image datasets show that PPCANet outperforms state-of-the-art attention-based networks and recent deep neural networks. We also provide visual analysis and ablation study to explain the behavior of PPCANet in the decision-making process. © 2023, CC BY.

关键词： Pixels

Local doping of an oxide semiconductor by voltage-driven splitting of anti-Frenkel defects

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arXiv 2025年

作者： He, Jiali Ludacka, Ursula Hunnestad, Kasper A. Småbråten, Didrik R. Shapovalov, Konstantin Vullum, Per Erik Hatzoglou, Constantinos Evans, Donald M. Roede, Erik D. Yan, Zewu Bourret, Edith Selbach, Sverre M. Gao, David Akola, Jaakko Meier, Dennis Department of Materials Science and Engineering NTNU Norwegian University of Science and Technology Trondheim Norway Department of Electronic Systems NTNU Norwegian University of Science and Technology Trondheim Norway Department of Sustainable Energy Technology SINTEF Industry Oslo Norway Theoretical Materials Physics Q-MAT University of Liège Sart-Tilman B-4000 Belgium SINTEF Industry Trondheim Norway Department of Physics ETH Zürich Zürich Switzerland Materials Sciences Division Lawrence Berkeley National Laboratory Berkeley United States Department of Physics NTNU Norwegian University of Science and Technology Trondheim Norway Nanolayers Research Computing London United Kingdom Computational Physics Laboratory Tampere University Tampere Finland

Layered oxides exhibit high ionic mobility and chemical flexibility, attracting interest as cathode materials for lithium-ion batteries and the pairing of hydrogen production and carbon capture. Recently, layered oxides emerged as highly tunable semiconductors. For example, by introducing anti-Frenkel defects, the electronic hopping conductance in hexagonal manganites was increased locally by orders of magnitude. Here, we demonstrate local acceptor and donor doping in Er(Mn,Ti)O3, facilitated by the splitting of such anti-Frenkel defects under applied d.c. voltage. By combining density functional theory calculations, scanning probe microscopy, atom probe tomography, and scanning transmission electron microscopy, we show that the oxygen defects readily move through the layered crystal structure, leading to nano-sized interstitial-rich (p-type) and vacancy-rich (n-type) regions. The resulting pattern is comparable to dipolar npn-junctions and stable on the timescale of days. Our findings reveal the possibility of temporarily functionalizing oxide semiconductors at the nanoscale, giving additional opportunities for the field of oxide electronics and the development of transient electronics in general. © 2025, CC BY.

关键词： Oxide semiconductors

Evaluation of Autonomous Vehicle Sensing and Compute Load on a Chassis Dynamometer

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Evaluation of Autonomous Vehicle Sensing and Compute Load on...

International Conference on Intelligent Transportation

作者： Nicholas E. Brown Farhang Motallebiaraghi Johan Fanas Rojas Sherif Ayantayo Richard Meyer Zachary D Asher Ali Riza Ekti Chieh Ross Wang Nicholas A. Goberville Ben Feinberg Mechanical and Aerospace Engineering Department Western Michigan University Kalamazoo Michigan USA Oak Ridge National Laboratory Energy Science and Technology Directorate Oak Ridge Tennessee USA Transportation and Power Systems Division Argonne National Laboratory Lemont Illinois USA Center for Computing Research Sandia National Laboratories Albuquerque New Mexico USA

The sensing and compute load auxiliary energy consumption in autonomous vehicles may be significant due to the large number of sensors and the high compute load from sensor processing and route planning. To understand this issue, this study investigates the top-down energy usage of an electric 2015 Kia Soul fully instrumented with state sensors and a state-specific computer for path planning and sensor processing. A chassis dynamometer was then used to evaluate the cases of (1) no sensors or computation, (2) only sensors operating, and (3) sensors plus compute load. The vehicle was operated autonomously on the dynamometer using a PolySync drive-kit with drive-by-wire longitudinal control. The DynoJet model 224xLC was used to adapt the eddy current dynamometer's road load parameters to comply with an Environmental Protection Agency drive schedule and to evaluate performance against the Argonne National laboratory Digital Dynamometer Dataset. On the UDDS-HWFET combined driving cycle, the stock battery's range was reduced by 5.6% for sensors alone and 12.2% for sensors and compute load. These results show that the added sensing and compute auxiliary load from automated and autonomous systems is significant and that research efforts need to be spent investigating new energy efficient systems.

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Forecasting COVID-19 Infections in Gulf Cooperation Council (GCC) Countries using Machine Learning

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arXiv 2023年

作者： Ismail, Leila Materwala, Huned Hennebelle, Alain Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates Independent Data Scientist and Engineer Al Ain Abu Dhabi United Arab Emirates

COVID-19 has infected more than 68 million people worldwide since it was first detected about a year ago. Machine learning time series models have been implemented to forecast COVID-19 infections. In this paper, we develop time series models for the Gulf Cooperation Council (GCC) countries using the public COVID-19 dataset from Johns Hopkins. The dataset set includes the one-year cumulative COVID-19 cases between 22/01/2020 to 22/01/2021. We developed different models for the countries under study based on the spatial distribution of the infection data. Our experimental results show that the developed models can forecast COVID-19 infections with high *** Codes 68-04, 68T05, 68T07, 68T20 © 2023, CC BY-NC-ND.

关键词： Coronavirus