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A Lightweight Convolutional Neural Network with Representation Self-challenge for Fingerprint Liveness Detection

Computers, Materials & Continua 2022年第10期73卷 719-733页

作者： Jie Chen Chengsheng Yuan Chen Cui Zhihua Xia Xingming Sun Thangarajah Akilan School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Ministry of Public SecurityZhejiang Police CollegeHangzhou310053China Jiangsu Yuchi Blockchain Research Institute Nanjing210044China Department of Software Engineering Lakehead UniversityThunder BayON P7B 5E1Canada

Fingerprint identification systems have been widely deployed in many occasions of our daily ***,together with many advantages,they are still vulnerable to the presentation attack(PA)by some counterfeit *** address challenges from PA,fingerprint liveness detection(FLD)technology has been proposed and gradually attracted people’s *** vast majority of the FLD methods directly employ convolutional neural network(CNN),and rarely pay attention to the problem of overparameterization and over-fitting of models,resulting in large calculation force of model deployment and poor model *** at filling this gap,this paper designs a lightweight multi-scale convolutional neural network method,and further proposes a novel hybrid spatial pyramid pooling block to extract abundant features,so that the number of model parameters is greatly reduced,and support multi-scale true/fake fingerprint ***,the representation self-challenge(RSC)method is used to train the model,and the attention mechanism is also adopted for optimization during execution,which alleviates the problem of model over-fitting and enhances generalization of detection ***,experimental results on two publicly benchmarks:LivDet2011 and LivDet2013 sets,show that our method achieves outstanding detection results for blind materials and *** size of the model parameters is only 548 KB,and the average detection error of cross-sensors and cross-materials are 15.22 and 1 respectively,reaching the highest level currently available.

关键词： FLD lightweight multi-scale RSC blind materials

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A Research on application Server-Oriented Digital Forensics in New Types of Network Crime

A Research on Application Server-Oriented Digital Forensics ...

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IEEE Advanced information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Bozheng Liao Guangxuan Chen Jiajian Wu Yingying Xu Qiang Liu Key Laboratory of Public Security Information Application Based on Big-data Architecture Ministry of Public Security Zhejiang Police College Hangzhou China

ISBN: (纸本)9781665479691

In the process of application server investigation and evidence collection of new types of network crime, this paper summarizes the analysis of common server clusters, virtual private network servers and IP-based voice transmission servers in public security investigation and forensic identification. based on the case analysis and the actual process of case investigation, a new type of network crime application server inspection method is proposed, which can be used to analyze new types of application servers in the process of law enforcement inspection and digital forensics practice.

关键词： Strips Digital forensics Inspection Search engines Virtual private networks Safety Servers

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Intelligence Analysis of Crime of Helping Cybercrime Delinquency based on Knowledge Ggraph

Intelligence Analysis of Crime of Helping Cybercrime Delinqu...

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IEEE Advanced information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Qinhui Qi Guangxuan Chen Fangyi Ye Zihao Lin Qiang Liu Key Laboratory of Public Security Information Application Based on Big-data Architecture Ministry of Public Security Zhejiang Police College Hangzhou China

ISBN: (纸本)9781665479691

This paper studies the crime of helping cybercrime delinquency (CHCD) from two aspects: theoretical literature research and case empirical research. First of all, clarify the basic concept, development trend and case characteristics of the crime of facilitation, and make a comprehensive exposition of the crime of facilitation. Secondly, the basic situation analysis, co-word analysis, and keyword clustering of the theoretical research literature on the CHCD in the literature database in recent years are carried out, so as to obtain the research hotspots and evolution process of the CHCD, and analyze the research in the form of knowledge graph. Then, through the analysis and research of the crime cases, the relevant data and information are extracted from the judgment documents, and the regional differences of the crime, the characteristics of the criminal subject, the characteristics of the criminal behavior, and the judicial sentencing are comprehensively sorted out to provide information for the follow-up intelligence research and judgment support.

关键词： Automation databases Market research information management Behavioral sciences data mining Computer crime

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A novel high-fidelity reversible data hiding scheme based on multi-classification pixel value ordering

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Journal of Visual Communication and Image Representation 2025年 110卷

作者： Cui, Chen Li, Li Lu, Jianfeng Zhang, Shanqing Chang, Chin-Chen College of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Zhejiang Police College Hangzhou310018 China Department of Information Engineering and Computer Science Feng Chia University Taichung407 Taiwan

Pixel value ordering (PVO) is a highly effective technique that employs a pixel block partitioning and sorting for reversible data hiding (RDH). However, its embedding performance is significantly impacted by block size. To address this, an improved pixel-based PVO (IPPVO) was developed adopting a per-pixel approach and adaptive context size. Nevertheless, IPPVO only considers pixels below and to the right for prediction, neglecting other closer neighboring regions, leading to inaccurate predictions. This study presents a RDH strategy using multi-classification embedding to enhance performance. First, pixels are categorized into four classes based on parity coordinates, obtaining higher correlation prediction values using an adaptive nearest neighbor content size. Second, a new complexity calculation method is introduced, the complexity frequency of pixel regions to better differentiate between complex and flat regions. Finally, an effective embedding ratio and index value constraint are introduced to mitigate the challenge of excessive distortion when embedding large capacities. Experimental results indicate that the proposed scheme offers superior embedding capacity with low distortion compared to state-of-the-art PVO-based RDH methods. © 2025 Elsevier Inc.

关键词： Adaptive modification High-fidelity Multi-classification RDH

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High-speed and long-reach PON employing PM-DQPSK signal and coherent receiver

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Journal of Optical Communications 2023年第s1期44卷 S97-S105页

作者： Qin, Panke Meng, Hui Zhang, Lei Chen, Guangxuan College of Computer Science and Technology Henan Polytechnic University Henan Jiaozuo454000 China Joint Logistics College National Defence University Beijing100858 China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Ministry of Public Security Zhejiang Police College Zhejiang Hangzhou310053 China

The development of communication technology requires a new type of passive optical network (PON) architecture. The architecture needs to be based on time division multiplexing (TDM) of 40 Gbit/s downstream and 10 Gbit/s upstream, and it can realize TDM PON prototype. The PON system achieved the system distance of 120 km supporting 128 ONU with power budget of 53.5 dB via polarization multiplexing differential quadrature phase shift keying format signal which employs the coherent receiver and the digital signal processing. © 2023 Walter de Gruyter GmbH. All rights reserved.

关键词： Passive optical networks

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An Image Semantic Communication System based on GAN and Relative Position-Encoding

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IEEE Communications Letters 2025年

作者： Zhang, Ziqi Tan, Weijie Li, Chunguo Guizhou University College of Big Data and Information Engineering Guiyang550025 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China Ministry of Industry and Information Technology Laboratory of Cryptography Application Technology Innovation and Testing Verification Beijing100015 China Southeast University School of Information Science and Engineering Nanjing210096 China

In image semantic communication, the complex wireless channel environment leads to the loss of image details and performance degradation during transmission. To address this issue, we propose an image semantic communication system based on Generative Adversarial Networks (GAN) and Relative Position-Encoding (RPE), named GAN-RpeSC. Specifically, in the system design, we introduce the GAN network at the output of the semantic communication framework to enhance the image restoration. We integrate the RPE module into the Visual Transformer (ViT) module, adding the RPE module to its Multihead Self Attention (MHSA), so that the model can better capture the spatial location information of the image. Experimental results show that the proposed GAN-RpeSC has significant advantages in improving image transmission quality, with a 0.3 dB improvement in peak signal-to-noise ratio (PSNR) compared to the ViT module alone. © 1997-2012 IEEE.

关键词： Encoding (symbols)

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Long Text Classification based on BERT 5

Long Text Classification Based on BERT

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5th IEEE information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2021

作者： Weijie, DIng Yunyi, Li Jing, Zhang Xuchen, Shen Big-data and Network Security Research Institute Zhejiang Police College Hangzhou310053 China College of Information Engineering Zhejiang University of Technology Hangzhou310023 China Key Laboratory of Public Security Information Application Based on Big-data Architecture Ministry of Public Security Hangzhou310053 China Zhejiang Police College Department of Investigation Hangzhou310053 China Zhejiang Police College Department of Computer and Information Security Hangzhou310053 China Science and Technology Management Section Hangzhou311203 China

ISBN: (纸本)9781665415989

Existing text classification algorithms generally have limitations in terms of text length and yield poor classification results for long texts. To address this problem, we propose a BERT-based long text classification method. First, we slice the long text and use BERT to encode the sliced clauses to obtain the local semantic information. Second, we use BiLSTM to fuse the local semantic information and adopt the attention mechanism to increase the weight of important clauses in the long text, so as to obtain the global semantic information. Finally, the global semantic information is input to the softmax layer for classification. Experimental results show that the proposed method achieves higher accuracy than commonly used models. © 2021 IEEE.

关键词： Classification (of information)

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A Spam Classification Method based on Naive Bayes 6

A Spam Classification Method Based on Naive Bayes

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6th IEEE information Technology and Mechatronics Engineering Conference, ITOEC 2022

作者： Ye, Fangyi Chen, Guanxuan Liu, Qiang Zhang, Lei Qi, Qinhui Hu, Bo Fan, Xiaoshi Institute for Cyberspace Intelligence and Crime Governance Zhejiang Police College Hangzhou China Key Laboratory of Public Security Informatization Application Based on Big-Data Architecture MPS Zhejiang Police College Hangzhou China Joint Logistics College of National Defence University Beijing China Cadet Brigade 4 Joint Operations College of National Defence University Shijiazhuang China

ISBN: (纸本)9781665431859

Currently, spam will do everything possible to bypass or destroy the spam classifier. This paper proposes a naive Bayesian spam classification method, and designs Bernoulli naive Bayes algorithm and polynomial naive Bayes algorithm to classify English spam and Chinese spam respectively. Experiments show that the method proposed in this paper has a high precision rate and recall rate, and can classify spam simply, accurately and efficiently. © 2022 IEEE.

关键词： Classifiers

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Estimating China's poverty reduction efficiency by integrating multi-source geospatial data and deep learning techniques

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地球空间信息科学学报（英文版） 2024年第4期27卷 1000-1016页

作者： Yao Yao Jianfeng Zhou Zhenhui Sun Qingfeng Guan Zhiqiang Guo Yin Xu Jinbao Zhang Ye Hong Yuyang Cai Ruoyu Wang School of Geography and Information Engineering China University of GeosciencesWuhanChina Center for Spatial Information Science The University of TokyoChibaJapan Alibaba Group HangzhouChina Foshan Surveying Mapping and Geoinformation Research Institute FoshanChina Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities(Ministry of Natural Resources) School of Geographic SciencesEast China Normal UniversityShanghaChina School of Geography and Information Engineering China University of GeosciencesWuhanChina Institute of International Rivers and Eco-Security Yunnan UniversityKunmingChina Institute of Geography and Spatial Information School of Earth SciencesZhejiang UniversityHangzhouChina School of Geography and Planning Sun Yat-Sen UniversityGuangzhouChina Institute of Cartography and Geoinformation ETH ZurichZürichSwitzerland Department of Civil Environmental and Geomatic Engineering School of EngineeringUniversity College LondonLondonUK Centre for Public Health Queen's University BelfastBelfastNorthern IrelandUK

Poverty threatens human development especially for developing countries,so ending poverty has become one of the most important United Nations Sustainable Development Goals(SDGs).This study aims to explore China's progress in poverty reduction from 2016 to 2019 through time-series multi-source geospatial data and a deep learning *** poverty reduction efficiency(PRE)is measured by the difference in the out-of-poverty rates(which measures the probability of being not poor)of 2016 and *** study shows that the probability of poverty in all regions of China has shown an overall decreasing trend(PRE=0.264),which indicates that the progress in poverty reduction during this period is *** Hu Huanyong Line(Hu Line)shows an uneven geographical pattern of out-of-poverty rate between Southeast and Northwest *** 2016 to 2019,the centroid of China's out-of-poverty rate moved 105.786 km to the northeast while the standard deviation ellipse of the out-of-poverty rate moved 3 degrees away from the Hu Line,indicating that the regions with high out-of-poverty rates are more concentrated on the east side of the Hu Line from 2016 to *** results imply that the government's future poverty reduction policies should pay attention to the infrastructure construction in poor areas and appropriately increase the population density in poor *** study fills the gap in the research on poverty reduction under multiple scales and provides useful implications for the government's poverty reduction policy.

关键词： Poverty reduction efficiency(PRE) driving forces multisource big data deep learning random forest(RF)

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A Compressed Deep Convolutional Neural Networks for Face Recognition 5

A Compressed Deep Convolutional Neural Networks for Face Rec...

引用

5th IEEE International Conference on Cloud Computing and big data Analytics, ICCCBDA 2020

作者： Yao, Li Key Lab. of Pub. Secty. Info. Application Based on Big-data Architecture Ministry of Public Security Zhejiang Police College Hangzhou China

ISBN: (纸本)9781728160245

Recent years, deep convolutional neural network has led to significant improvements in face recognition and becomes one of the most popular techniques in computer vision community. However, deep CNN model requires vast amounts of data and time for training and deploying. To solve this problem, we present a deep compact convolutional neural network for face representation. First we apply PCA for initializing convolution filter. Then we adopt DCT and binary hashing for extracting face features. The models are trained on the CASIA-webFace datasets under Caffe framework. Experimental results show that the proposed methods achieve competitive accuracy on the LFW verification benchmark. © 2020 IEEE.

关键词： Face recognition

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