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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 Sketch-based Generation Model for Diverse Ceramic Tile Images Using Generative Adversarial Network

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Intelligent Automation & Soft Computing 2023年第9期37卷 2865-2882页

作者： Jianfeng Lu Xinyi Liu Mengtao Shi Chen Cui Mahmoud Emam School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Ministry of Public SecurityZhejiang Police CollegeHangzhou310000China Faculty of Artificial Intelligence Menoufia UniversityShebin El-Koom32511Egypt

Ceramic tiles are one of the most indispensable materials for interior *** ceramic patterns can’t match the design requirements in terms of diversity and interactivity due to their natural *** this paper,we propose a sketch-based generation method for generating diverse ceramic tile images based on a hand-drawn sketches using Generative Adversarial Network(GAN).The generated tile images can be tailored to meet the specific needs of the user for the tile *** proposed method consists of four ***,a dataset of ceramic tile images with diverse distributions is created and then pre-trained based on ***,for each ceramic tile image in the dataset,the corresponding sketch image is generated and then the mapping relationship between the images is trained based on a sketch extraction network using ResNet Block and jump connection to improve the quality of the generated ***,the sketch style is redefined according to the characteristics of the ceramic tile images and then double cross-domain adversarial loss functions are employed to guide the ceramic tile generation network for fitting in the direction of the sketch style and to improve the training ***,we apply hidden space perturbation and interpolation for further enriching the output textures style and satisfying the concept of“one style with multiple faces”.We conduct the training process of the proposed generation network on 2583 ceramic tile images *** measure the generative diversity and quality,we use Frechet Inception Distance(FID)and Blind/Referenceless Image Spatial Quality Evaluator(BRISQUE)*** experimental results prove that the proposed model greatly enhances the generation results of the ceramic tile images,with FID of 32.47 and BRISQUE of 28.44.

关键词： Ceramic tile pattern design cross-domain learning deep learning GAN generative adversarial networks ResNet Block

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A Compressed Deep Convolutional Neural Networks for Face Recognition 5

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

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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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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 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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A Knowledge Enhanced Pre-Training Model for Chinese Weibo Sentiment Analysis

A Knowledge Enhanced Pre-Training Model for Chinese Weibo Se...

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IEEE International Conference on Cloud Computing and big data Analysis (ICCCBDA)

作者： Li Yao Key Laboratory of Public Security Information Application Based on Big-data Architecture Ministry of Public Security Zhejiang Police College Hangzhou China

ISBN: (数字)9798350373554

ISBN: (纸本)9798350373561

Sentiment analysis aims to automatically identify and extract subjective information such as tendencies, stances, evaluations, and opinions from text. Current state-of-the-art Natural Language Processing (NLP) methods, primarily based on large-scale pre-trained language models (PLM), have significantly advanced the field of sentiment analysis. However, these methods often struggle due to the scarcity of annotated data. To address these challenges, we present a knowledge-enhanced pre-trained model for the Chinese social media platform. This model employs a phrase-based masking strategy and incorporates knowledge-related information by making adjustments to the structure of the intermediate encoder layer. We validated the model's performance through comparative experiments with other baseline methods, demonstrating the effectiveness of integrating sentiment resources into pre-trained language models.

关键词： Training Sentiment analysis Analytical models Social networking (online) Computational modeling Knowledge based systems Linguistics

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A Compressed Deep Convolutional Neural Networks for Face Recognition

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

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IEEE International Conference on Cloud Computing and big data Analysis (ICCCBDA)

作者： Li Yao Key Laboratory of Public Security Information Application Based on Big-data Architecture Ministry of Public Security Zhejiang Police College Hangzhou China

ISBN: (数字)9781728160245

ISBN: (纸本)9781728160252

关键词： Feature extraction Convolution Face recognition Convolutional neural networks Face Principal component analysis Biological neural networks

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IntML-KNN: A Few-Shot Radio Frequency Fingerprint Identification Scheme for Lora Devices

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IEEE Signal Processing Letters 2025年

作者： Cao, Xin Tan, Weijie Gao, Qiangqiang Hu, Zhilong Li, Chunguo Guizhou University College of Computer Science and Technology Guiyang 550025 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang 550025 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu 610025 China Ministry of Industry and Information Technology Laboratory of Cryptograph Application Technology Innovation and Testing Verification Beijing 100015 China Southeast University School of Information Science and Engineering Nanjing 210096 China

Deep learning (DL) is widely used in radio frequency fingerprint identification (RFFI). However, in few-shot case, traditional DL-based RFFI need to construct auxiliary dataset to realize radio frequency fingerprint identification. To address this issue, we propose a few-shot RFFI (FS-RFFI) method based on interpolating metric learning and KNN (IntML-KNN). Specifically, the method first extends the dataset with data augmentation, and CutMix interpolation. Secondly, combining with metric learning to enhance the generalization capacity of the model. Finally, KNN algorithm is designed to realize device classification and detection. The proposed IntML-KNN method is verified on the commercial available LoRa dataset. The experimental results indicate that the proposed scheme exhibits strong classification and generalization performance in FS-RFFI. Meanwhile, the recognition rate of the proposed IntML-KNN reaches 97.00% with only 10% samples. © 1994-2012 IEEE.

关键词： data augmentation deep learning few-shot LoRa metric learning radio frequency fingerprint

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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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Research on Digital Forensics Framework for Malicious Behavior in Cloud 4

Research on Digital Forensics Framework for Malicious Behavi...

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4th IEEE Advanced information Technology, Electronic and Automation Control Conference, IAEAC 2019

作者： Chen, Guangxuan Wu, Di Chen, Guangxiao Qin, Panke Zhang, Lei Liu, Qiang Zhejiang Police College Key Laboratory of Public Security Information Application Based on Big Data Architecture MPS Hangzhou China People's Procuratorate of Hangzhou Hangzhou China Wenzhou Public Security Bureau Wenzhou China Universidad Carlos III de Madrid Madrid Spain Henan Polytechnic University Jiaozuo China National Defence University Joint Services Academy Beijing China

ISBN: (纸本)9781728119076

The difficult of detecting, response, tracing the malicious behavior in cloud has brought great challenges to the law enforcement in combating cybercrimes. This paper presents a malicious behavior oriented framework of detection, emergency response, traceability, and digital forensics in cloud environment. A cloud-based malicious behavior detection mechanism based on SDN is constructed, which implements full-traffic flow detection technology and malicious virtual machine detection based on memory analysis. The emergency response and traceability module can clarify the types of the malicious behavior and the impacts of the events, and locate the source of the event. The key nodes and paths of the infection topology or propagation path of the malicious behavior will be located security measure will be dispatched timely. The proposed IaaS service based forensics module realized the virtualization facility memory evidence extraction and analysis techniques, which can solve volatile data loss problems that often happened in traditional forensic methods. © 2019 IEEE.

关键词： Cybersecurity

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