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CMCF-Net: An End-to-End Context Multiscale Cross-Fusion Network for Robust Copy-Move Forgery Detection

IEEE Transactions on Multimedia 2024年 26卷 6090-6101页

作者： Xiong, Lizhi Xu, Jianhua Yang, Ching-Nung Zhang, Xinpeng Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Engineering Research Center of Digital Forensics Ministry of Education Nanjing210044 China Nanjing University of Information Science and Technology Software Engineering Nanjing210044 China National Dong Hwa University Department of Computer Science and Information Engineering Hualien974301 Taiwan Fudan University School of Computer Science Shanghai200433 China

Image copy-move forgery detection (CMFD) has become a challenging problem due to increasingly powerful editing software that makes forged images increasingly realistic. Existing algorithms that directly connect multiple scales of features in the encoder part may not effectively aggregate contextual information, resulting in poor performance. In this paper, an end-to-end context multiscale cross-fusion network (CMCF-Net) is proposed to detect image copy-move forgery. The proposed network consists of a multiscale feature extraction fusion (MSF) module and a multi-information fusion decoding (MFD) module. Multiscale information is efficiently extracted and fused in the MSF module utilizing stacked-scale feature fusion, which improves the network's forgery localization ability on objects of different scales. The MFD module employs contextual information combination and weighted fusion of multiscale information to guide the network in obtaining relevant clues from correlated information at multiple different scales. Experimental results and analysis have demonstrated that the proposed CMCF-Net achieves the best localization results with higher robustness. © 1999-2012 IEEE.

关键词： Feature extraction

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Hetu: a highly efficient automatic parallel distributed deep learning system

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science China(Information sciences) 2023年第1期66卷 314-315页

作者： Xupeng MIAO Xiaonan NIE Hailin ZHANG Tong ZHAO Bin CUI School of Computer Science Peking University Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications

On July 18, 2021, the PKU-DAIR Lab1)(Data and Intelligence Research Lab at Peking University) openly released the source code of Hetu, a highly efficient and easy-to-use distributed deep learning(DL) framework. Hetu is the first distributed DL system developed by academic groups in Chinese universities, and takes into account both high availability in industry and innovation in academia. Through independent research and development, Hetu is completely decoupled from the existing DL systems and has unique characteristics. The public release of the Hetu system will help researchers and practitioners to carry out frontier MLSys(machine learning system) research and promote innovation and industrial upgrading.

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High quality factor microwave sensor for ethanol solution concentration detection 10

High quality factor microwave sensor for ethanol solution co...

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10th International Symposium on Next Generation Electronics, ISNE 2022

作者： Zhang, Fan Chen, Yuting Lin, Song Wu, Enkang Liang, Junge Huang, Xiang-Lin Communication University of China School of Computer Science & Cybersecurity Beijing China Jiangnan University School of Internet of Things Engineering Wuxi China

ISBN: (纸本)9781665455251

When using microwave sensors to detect the concentration of a solution, two important objectives to consider are resolution and sensitivity. This paper presents a bent-wire based microwave sensor that combined an active feedback loop to increase the quality factor from 47 to 295. The external capacitance changes the resonant frequency to study the influence on the detection sensitivity. The resonant frequency of the active microwave sensor gradually increased from 2.62GHz to 3.10GHz with capacitance of 24pF,2.4pF , and 1pF respectively. The resonant frequency without external capacitor is 2.26 GHz. Active microwave sensors at four resonant frequencies were used for sensing ethanol solutions from 0 to 100% concentration. When characterized by frequency shift, the sensitivity increased from 0.17MHz% to 0.34MHz% with increasing frequency, and when characterized by magnitude change, the sensitivity peaked at 0.14 dB% at the detection frequency of 2.62GHz, in agreement with the theoretical analysis. This result shows that active microwave sensors, combined with the frequency correction effect of chip capacitors, can effectively improve sensing sensitivity. © 2023 IEEE.

关键词： Capacitance

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Towards intelligent and trustworthy task assignments for 5G-enabled industrial communication systems

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Digital Communications and Networks 2025年第1期11卷 246-255页

作者： Mingfeng Huang Anfeng Liu Neal N.Xiong Athanasios V.Vasilakos School of Computer and Communication Engineering Changsha University of Science&TechnologyChangsha410114China School of Electronic Information Central South UniversityChangsha410083China Department of Computer Science and Mathematics Sul Ross State UniversityAlpineTX 79830USA College of Mathematics and Computer Science Fuzhou UniversityFuzhou350116China Center for AI Research University of AgderGrimstad 999026Norway

With the unprecedented prevalence of Industrial Internet of Things(IIoT)and 5G technology,various applications supported by industrial communication systems have generated exponentially increased processing tasks,which makes task assignment inefficient due to insufficient *** this paper,an Intelligent and Trustworthy task assignment method based on Trust and Social relations(ITTS)is proposed for scenarios with many tasks and few ***,ITTS first makes initial assignments based on trust and social influences,thereby transforming the complex large-scale industrial task assignment of the platform into the small-scale task assignment for each ***,an intelligent Q-decision mechanism based on workers'social relation is proposed,which adopts the first-exploration-then-utilization principle to allocate *** when a worker cannot cope with the assigned tasks,it initiates dynamic worker recruitment,thus effectively solving the worker shortage problem as well as the cold start *** importantly,we consider trust and security issues,and evaluate the trust and social circles of workers by accumulating task feedback,to provide the platform a reference for worker recruitment,thereby creating a high-quality worker ***,extensive simulations demonstrate ITTS outperforms two benchmark methods by increasing task completion rates by 56.49%-61.53%and profit by 42.34%-47.19%.

关键词： Industrial Internet of Things Insufficient workers Trust evaluation Social relation Task assignment

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Efficient Communication in Wireless Sensor Networks Using Optimized Energy Efficient Engroove Leach Clustering Protocol

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Tsinghua science and Technology 2024年第4期29卷 985-1001页

作者： N.Meenakshi Sultan Ahmad A.V.Prabu J.Nageswara Rao Nashwan Adnan Othman Hikmat A.M.Abdeljaber R.Sekar Jabeen Nazeer SRM Institute of Science and Technology KattankulathurChennai 603203India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj 11942Saudi Arabia University Center for Research and Development(UCRD) Department of Computer Science and EngineeringChandigarh UniversityGharuanMohali 140413India Department of ECE Koneru Lakshmaiah Education FoundationVaddeswaramGuntur 522502India Department of Computer Science and Engineering Lakireddy Balireddy College of Engineering(A)NTR District 521230India Department of Computer Engineering College of EngineeringKnowledge UniversityErbil 44001Iraq Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmman 11937Jordan Department of Electronics and Communication Engineering(ECE) School of EngineeringPresidency UniversityBangalore 834001India

The Wireless Sensor Network(WSN)is a network that is constructed in regions that are inaccessible to human *** widespread deployment of wireless micro sensors will make it possible to conduct accurate environmental monitoring for a use in both civil and military *** make use of these data to monitor and keep track of the physical data of the surrounding environment in order to ensure the sustainability of the *** data have to be picked up by the sensor,and then sent to the sink node where they may be *** nodes of the WSNs are powered by batteries,therefore they eventually run out of *** energy restriction has an effect on the network life span and environmental *** objective of this study is to further improve the Engroove Leach(EL)protocol’s energy efficiency so that the network can operate for a very long time while consuming the least amount of *** lifespan of WSNs is being extended often using clustering and routing *** Meta Inspired Hawks Fragment Optimization(MIHFO)system,which is based on passive clustering,is used in this study to do *** cluster head is chosen based on the nodes’residual energy,distance to neighbors,distance to base station,node degree,and node *** on distance,residual energy,and node degree,an algorithm known as Heuristic Wing Antfly Optimization(HWAFO)selects the optimum path between the cluster head and Base Station(BS).They examine the number of nodes that are active,their energy consumption,and the number of data packets that the BS *** overall experimentation is carried out under the MATLAB *** the analysis,it has been discovered that the suggested approach yields noticeably superior outcomes in terms of throughput,packet delivery and drop ratio,and average energy consumption.

关键词： wireless sensor networks energy efficient engroove leach protocol meta inspired Hawks fragment optimization heuristic wing antfly optimization

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Adversarial Profiled Side-Channel Attack with Unsupervised Domain Adaptation 9

Adversarial Profiled Side-Channel Attack with Unsupervised D...

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9th International Conference on computer and Communications, ICCC 2023

作者： Meng, Fanfei Li, Zhanbo Mao, Baolei Hu, Wei Qin, Maoyuan Zhou, Qiang Zhengzhou University School of Cyber Science and Engineering Zhengzhou China Northwestern Polytechnical University School of Cybersecurity Xi'an China Xi'an Technological University School of Computer Science and Engineering Xi'an China

ISBN: (纸本)9798350317251

Deep learning-based methods have been proven to be very successful when applied to profiled side-channel attacks (SCA). In profiled SCA, the attacker gains knowledge about the target device by accessing a profiling device before the attack. However, most of the work does not consider the problem that it is difficult to obtain profiling traces that is fully consistent with the attack traces because the target device often employs more advanced masking countermeasures. Even if a small discrepancy between profiling traces and attack traces (regarded as domain discrepancy) may cause the attack failure. To address the problem of domain discrepancy, we propose a side-channel attack method based on the domain adversarial self-attention mechanism (DASA-SCA). Specifically, the method feeds the feature extractor through the bounded loss of the domain classifier when building the model in order to learn a hidden representation that is not only discriminative but also domain invariant. To enhance this domain-invariant representation, we introduce self-attention mechanism in the feature extractor to give higher weights to the significant parts of the leakage in order to improve the feature extraction. We implement three different deep learning models including CNN, ResNet, and LSTM in the DASA-SCA approach for both electromagnetic(EM) side-channel and power side-channel to demonstrate the scalability and generality of the framework. Finally, we validate the effectiveness of the DASA-SCA approach using the ASCAD dataset and DPA dataset. The experimental results demonstrate that our method can significantly reduce the impact of domain discrepancy caused by different countermeasures and enhance the side-channel attack capability. © 2023 IEEE.

关键词： Side channel attack

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Data quantity governance for machine learning in materials science

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National science Review 2023年第7期10卷 234-250页

作者： Yue Liu Zhengwei Yang Xinxin Zou Shuchang Ma Dahui Liu Maxim Avdeev Siqi Shi School of Computer Engineering and Science Shanghai University Shanghai Engineering Research Center of Intelligent Computing System Australian Nuclear Science and Technology Organisation School of Chemistry The University of Sydney State Key Laboratory of Advanced Special Steel School of Materials Science and Engineering Shanghai University Materials Genome Institute Shanghai University

Data-driven machine learning(ML) is widely employed in the analysis of materials structure-activity relationships,performance optimization and materials design due to its superior ability to reveal latent data patterns and make accurate ***,because of the laborious process of materials data acquisition,ML models encounter the issue of the mismatch between a high dimension of feature space and a small sample size(for traditional ML models) or the mismatch between model parameters and sample size(for deep-learning models),usually resulting in terrible ***,we review the efforts for tackling this issue via feature reduction,sample augmentation and specific ML approaches,and show that the balance between the number of samples and features or model parameters should attract great attention during data quantity *** this,we propose a synergistic data quantity governance flow with the incorporation of materials domain *** summarizing the approaches to incorporating materials domain knowledge into the process of ML,we provide examples of incorporating domain knowledge into governance schemes to demonstrate the advantages of the approach and *** work paves the way for obtaining the required high-quality data to accelerate materials design and discovery based on ML.

关键词： machine learning data governance data quantity materials science

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Machine learning design of 400 MPa grade biodegradable Zn-Mn based alloys with appropriate corrosion rates

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International Journal of Minerals,Metallurgy and Materials 2024年第12期31卷 2727-2736页

作者： Wangzhang Chen Wei Gou Yageng Li Xiangmin Li Meng Li Jianxin Hou Xiaotong Zhang Zhangzhi Shi Luning Wang Beijing Advanced Innovation Center for Materials Genome Engineering State Key Laboratory for Advanced Metals and MaterialsSchool of Materials Science and EngineeringUniversity of Science and Technology BeijingBeijing 100083China Institute of Materials Intelligent Technology Liaoning Academy of MaterialsShenyang 110004China School of Computer and Communication Engineering Shunde Innovation SchoolUniversity of Science and Technology BeijingShunde 528399China

The commonly used trial-and-error method of biodegradable Zn alloys is costly and *** this study,based on the self-built database of biodegradable Zn alloys,two machine learning models are established by the first time to predict the ultimate tensile strength(UTS)and immersion corrosion rate(CR)of biodegradable Zn alloys.A real-time visualization interface has been established to design Zn-Mn based alloys;a representative alloy is *** tensile mechanical properties and immersion corrosion rate tests,its UTS reaches 420 MPa,and the prediction error is only 0.95%.CR is 73μm/a and the prediction error is 5.5%,which elevates 50 MPa grade of UTS and owns appropriate corrosion ***,influences of the selected features on UTS and CR are discussed in *** combined application of UTS and CR model provides a new strategy for synergistically regulating comprehens-ive properties of biodegradable Zn alloys.

关键词： Zn alloys machine learning alloy design performance prediction

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Robust WiFi Sensing-Based Human Pose Estimation Using Denoising Autoencoder and CNN With Dynamic Subcarrier Attention

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IEEE Internet of Things Journal 2025年第11期12卷 17066-17079页

作者： Hoang Nguyen, Xuan Nguyen, Van-Dinh Luu, Quang-Trung Dinh Gian, Toan Shin, Oh-Soon College of Engineering and Computer Science Center for Environmental Intelligence VinUniversity Hanoi100000 Viet Nam Hanoi University of Science and Technology School of Electrical and Electronic Engineering Hanoi100000 Viet Nam Soongsil University School of Electronic Engineering Seoul06978 Korea Republic of

WiFi sensing-based human pose estimation (HPE) has gained significant attention in the academic community due to its advantages over vision- and sensor-based methods, including nonintrusiveness, convenience, and enhanced privacy protection. However, most existing WiFi-based pose Estimators suffer from poor performance and lack robustness in the presence of random noise. To address these challenges, this article presents a novel HPE architecture comprising two key modules: 1) a Denoiser and 2) an Estimator. The Denoiser is based on an autoencoder structure, while the Estimator is based on a new convolutional neural network (CNN) called SDy-CNN, which is designed to dynamically focus on high-information subcarriers of orthogonal frequency division multiplexing signals. Additionally, Bayesian optimization is employed to fine-tune the architecture’s parameters for optimal performance flexibly. Experiments conducted on a comprehensive dataset, MM-Fi, demonstrate that the proposed architecture significantly outperforms existing state-of-the-art Estimators, achieving up to an 8.38% improvement in HPE accuracy in clean data scenarios and up to a 14% improvement in noisy data scenarios. It has also been proven to gain computational efficiency when being much faster than other methods. © 2014 IEEE.

关键词： Wi-Fi

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A deep learning framework for enhanced mass spectrometry data analysis and biomarker screening

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computer Methods in Biomechanics and Biomedical engineering 2025年 1-13页

作者： Zhang, Shuyu Li, Zhiyu Peng, Weili Chen, Yuanyuan Wu, Yao Machine Intelligence Lab College of Computer Science Sichuan University Chengdu China National Engineering Research Center for Biomaterials School of Biomedical Engineering Sichuan University Chengdu China

Mass spectrometry (MS) serves as a powerful analytical technique in metabolomics. Traditional MS analysis workflows are heavily reliant on operator experience and are prone to be influenced by complex, high-dimensional MS data. This study introduces a deep learning framework designed to enhance the classification of complex MS data and facilitate biomarker screening. The proposed framework integrates preprocessing, classification, and biomarker selection, addressing challenges in high-dimensional MS analysis. Experimental results demonstrate significant improvements in classification tasks compared to other machine learning approaches. Additionally, the proposed peak-preprocessing module is validated for its potential in biomarker screening, identifying potential biomarkers from high-dimensional data. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Liver disease deep learning mass spectrometry pre-processing

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