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DFMC:Feature-Driven Data-Free Knowledge Distillation

IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Zhang, Zherui Xu, Rongtao Wang, Changwei Xu, Wenhao Chen, Shunpeng Xu, Shibiao Xu, Guangyuan Guo, Li Beijing University of Posts and Telecommunications School of Artificial Intelligence Beijing100876 China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China Qilu University of Technology Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250316 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan250014 China

Data-Free Knowledge Distillation (DFKD) enables knowledge transfer from teacher networks without access to the real dataset. However, generator-based DFKD methods often suffer from insufficient diversity or low-confidence in synthetic images, negatively impacting student network performance. This paper introduces DFMC, a generative feature-driven framework to mitigate the inherent limitations of DFKD. We propose exploiting semantic description between generative feature domains to guide augmentation strategies, avoiding random abstract inputs caused by inconsistent semantic quality. Then, by applying noise to the generative features, we produce contrastive learning pairs indirectly, limiting the sampling range of the feature domain to encourage the student network to learn domain-invariant features. Finally, we guide the student network to deeply mimic the teacher’s layer-wise implicit classification behavior for the augmented synthetic images. Extensive experiments across various datasets and downstream tasks demonstrate the effectiveness of DFMC, achieving significant improvements while preventing student networks from overfitting to semantic ambiguous images. © 1991-2012 IEEE.

关键词： Knowledge transfer

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MSONoC: A Metasurface-assisted Hybrid Waveguide/FSO network-on-Chip Architecture

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and networking 2024年

作者： Sun, Wei Li, Jingsi Guo, Pengxing He, Xiangyu Wang, Zimo Hu, Cong Liu, Weichen Hou, Weigang Guo, Lei School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Institute of Intelligent Communications and Network Security Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology Guilin541004 China School of Computer Science and Engineering Nanyang Technological University Singapore

Optical networks-on-chips (ONoCs) have emerged as the most promising solution for on-chip interconnection networks, offering distinct advantages such as high bandwidth and low latency. However, traditional wired-based ONoCs often suffer from a high average number of hops, leading to challenges such as increased insertion loss, crosstalk noise, and energy consumption. Therefore, this paper presents a novel ONoC structure known as the metasurface ONoC (MSONoC), which integrates wired waveguide and free-space optical (FSO) transmission with the assistance of metasurfaces. Specifically, this work encompasses the design of a passive metasurface structure tailored for ONoCs, a virtual network cluster partitioning method, an optical metasurface access points select strategy, and a shortest-path routing algorithm. Performance verification is conducted using a modified Noxim simulator. The simulation results demonstrate that the proposed MSONoC offers advantages over traditional NoCs, radio frequency (RF)-based hybrid wired/wireless NoCs, and wired-based ONoCs in terms of throughput, delay, energy consumption, and signal-to-noise ratio under both synthetic traffic and real traffic models. These improvements proves the effectiveness of the MSONoC architecture in optimizing network performance, making it a promising solution for high-performance, energy-efficient communication in on-chip networks. © 2017 IEEE.

关键词： network-on-chip

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SAR Image Scene Classification Based on Metric Learning and CNNs

SAR Image Scene Classification Based on Metric Learning and ...

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Information Communication and Software Engineering (ICICSE), IEEE International Conference on

作者： Yaozhong Wang Zhiqiang Zhao Lei Wang School of Computer Science and Engineering and the Shaanxi Key Laboratory of Network Computing and Security Technology Xi‘an University of Technology Xi'an China School of Mathematics and Computer Science Shaanxi University of Technology Hanzhong China

ISBN: (纸本)9781665430302

With the development of synthetic aperture radar (SAR) imaging techniques, many high-quality images are acquired for exactly earth observation. As a result of heavy speckle, it is difficult to extract effective representation of different scene images to distinguish them accurately. In this paper, a metric-learning based convolutional neural network (CNN) is proposed to characterize SAR images. Specifically, a regularization term is proposed, which is constrained by the Mahalanobis distance. The regularization term learns a new metric space based on CNN, where the similar images should be close and those of dissimilar images should be separated. Comparing with the other approaches, experimental results of the proposed approach illustrate a superior classification for different scenes.

关键词： Earth Image analysis Conferences Speckle Extraterrestrial measurements Radar polarimetry Convolutional neural networks

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A Prior Embedding-Driven Architecture for Long Distance Blind Iris Recognition

arXiv

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arXiv 2024年

作者： Xiong, Qi Zhang, Xinman Shen, Jun International College Hunan University of Arts and Sciences Changde415000 China School of Automation Science and Engineering Faculty of Electronic and Information Engineering MOE Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Shaanxi Xi'An710049 China School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia

Blind iris images, which result from unknown degradation during the process of iris recognition at long distances, often lead to decreased iris recognition rates. Currently, little existing literature offers a solution to this problem. In response, we propose a prior embedding-driven architecture for long distance blind iris recognition. We first proposed a blind iris image restoration network called Iris-PPRGAN. To effectively restore the texture of the blind iris, Iris-PPRGAN includes a Generative Adversarial network (GAN) used as a Prior Decoder, and a DNN used as the encoder. To extract iris features more efficiently, we then proposed a robust iris classifier by modifying the bottleneck module of InsightFace, which called Insight-Iris. A low-quality blind iris image is first restored by Iris-PPRGAN, then the restored iris image undergoes recognition via Insight-Iris. Experimental results on the public CASIA-Irisdistance dataset demonstrate that our proposed method significantly superior results to state-of-the-art blind iris restoration methods both quantitatively and qualitatively, Specifically, the recognition rate for long-distance blind iris images reaches 90% after processing with our methods, representing an improvement of approximately ten percentage points compared to images without restoration. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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A Money Laundering Structure Detection Method Based on Incremental Transaction Analysis

A Money Laundering Structure Detection Method Based on Incre...

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Data Science and Information technology (DSIT), International Conference on

作者： Yifei Meng Zhong Li Key Laboratory of Embedded System and Service Computing Ministry of Education National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University Shanghai China College of Information Science and Technology Donghua University Shanghai China

Money laundering is the process of legitimizing dirty money through complex transactions, posing a serious threat to a country’s financial stability and national security. Nowadays, with the prevalence of organized money laundering, launderers prefer to use intricate multi-hop laundering chains to transfer dirty money. Moreover, they engage in normal financial activities to disrupt detection by auditors. In response to these trends and challenges, we propose a novel unsupervised money laundering structure detection framework for the anti-money laundering field, called structure incremental expansion (SIE). Our framework consists of two main modules: 1) Initialization of suspicious structures, which adopts a control-limit based method to identify suspicious accounts exhibiting anomalous transaction behavior. These accounts will serve as the starting points for suspicious structure expansion. 2) Dynamic structure expansion, where we design three dynamic membership functions according to the Financial Action Task Force’s definitions of the three stages of money laundering evolution. Newly-added incremental transactions in the network will be assigned to appropriate expanding suspicious structures. We conduct extensive experiments on simulated and public financial networks. SIE exhibits desirable performance and scalability. We also provide a detailed case study, visualizing a complete money laundering structure detection process, demonstrating our method’s strong interpretability.

关键词：

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Efficient poisoning attacks and defenses for unlabeled data in DDoS prediction of intelligent transportation systems

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security and Safety 2022年第1期1卷 121-141页

作者： Zhong Li Xianke Wu Changjun Jiang College of Information Science and Technology Donghua University Key Laboratory of the Ministry of Education for Embedded System and Service Computing Department of Computer Science Tongji University Shanghai Network Financial Security Collaborative Innovation Center Tongji University

Nowadays, large numbers of smart sensors(e.g., road-side cameras) which communicate with nearby base stations could launch distributed denial of services(DDoS) attack storms in intelligent transportation systems. DDoS attacks disable the services provided by base stations. Thus in this paper, considering the uneven communication traffic flows and privacy preserving, we give a hidden Markov model-based prediction model by utilizing the multi-step characteristic of DDoS with a federated learning framework to predict whether DDoS attacks will happen on base stations in the future. However, in the federated learning,we need to consider the problem of poisoning attacks due to malicious participants. The poisoning attacks will lead to the intelligent transportation systems paralysis without security protection. Traditional poisoning attacks mainly apply to the classification model with labeled data. In this paper, we propose a reinforcement learning-based poisoning method specifically for poisoning the prediction model with unlabeled data. Besides, previous related defense strategies rely on validation datasets with labeled data in the server. However, it is unrealistic since the local training datasets are not uploaded to the server due to privacy preserving, and our datasets are also unlabeled. Furthermore, we give a validation dataset-free defense strategy based on Dempster–Shafer(D–S) evidence theory avoiding anomaly aggregation to obtain a robust global model for precise DDoS prediction. In our experiments, we simulate 3000 points in combination with DARPA2000 dataset to carry out evaluations. The results indicate that our poisoning method can successfully poison the global prediction model with unlabeled data in a short time. Meanwhile, we compare our proposed defense algorithm with three popularly used defense algorithms. The results show that our defense method has a high accuracy rate of excluding poisoners and can obtain a high attack prediction probability.

关键词： Poisoning attacks Defenses Multi-step DDoS prediction Unlabeled data Intelligent transportation systems

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Visual malware classification and detection method based on convolutional neural network

Visual malware classification and detection method based on ...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Hua Huang Hua Sheng Xingwang Han Wenbin Zhang Zilin Zhao Information and Telecommunications Company State Grid Shandong Electric Power Company Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

With the rapid development of Internet technology, leading to the evolution of malware variants, and malicious threats are also increasing. Therefore, rapid and accurate classification of malware becomes crucial. In response to this challenge, this paper proposes a malware classification method based on deep learning and visualization techniques. The method first extracts sample features, then converts malware binary samples into image format, thus transforming abstract binary data into images with visual information. Subsequently, color mapping technique is applied to convert the processed images into color images. Furthermore, to address imbalanced datasets, this paper introduces data augmentation methods to ensure more balanced and comprehensive training of the model across different sample categories. Finally, an improved convolutional neural network architecture is employed for the classification detection of malware families, achieving accurate classification and identification of different malware families through learning and analysis of sample features. To evaluate the effectiveness of the proposed method, experiments are conducted on the Google Code Jam (GCJ) benchmark dataset. The experimental results demonstrate an accuracy rate of 99.29% on the GCJ dataset. Additionally, we also compared the performance of the improved method before and after, and found that the proposed method can provide more comprehensive feature representation and significantly improve classification accuracy and detection efficiency.

关键词： Deep learning Training Visualization Accuracy Color Static analysis Feature extraction Malware Internet Convolutional neural networks

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Multi-Channel Hypergraph network for Sequential Diagnosis Prediction in Healthcare

Multi-Channel Hypergraph Network for Sequential Diagnosis Pr...

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International Conference on Computer Supported Cooperative Work in Design

作者： Xin Zhang Xueping Peng Weimin Chen Weiyu Zhang Xiaoqiang Ren Wenpeng Luv Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia Shandong Mental Health Center Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Sequential diagnosis prediction (SDP) is a complex and challenging task, aming to predict future diagnoses of patients by analyzing their historical medical records. Although graph neural networks(GNNs) has been applied to successfully address the challenge of heterogeneous data integration in electronic health records, relatively limited work has been done on GNNs for sequential diagnosis prediction. Graph neural network-based methods, aimed at capturing structural and relational patterns of EHR data for sequential diagnosis prediction, explore code-code pairwise relationships, resulting in an inability to learn fine-grained, higher-order interaction relationships among different types medical codes. As a result, they are difficult to effectively model complex, multi-dimensional interactions among different types of medical codes necessary for accurate and nuanced diagnosis predictions. To address these challenges, this paper proposes a novel approach called Multi-Channel Hypergraph network (MCHN) predictive framework for sequential diagnosis prediction. The proposed method aims to explore the fine-grained higher-order interactions between different types of medical codes via multi-channel hypergraphs. Specifically, MCHN learns two levels of code embeddings from multi-channel hypergraph learning module and line graph learning module, respectively: (i) multi-channel hypergraph learning module, which is to learn multi-channel hypergraph level code embeddings by modeling the higher-order relationships between medical codes in different hypergraphs; and (ii) line graph learning module, which is to learn the line graph level code embeddings by modeling code-code pairwise relationships. In MCHN, we propose a novel channel-level attention mechanism to help our model attend to the informativeness of the different channel for forecasting future patient diagnoses. We also design a code-level attention mechanism, which can to pay more attention to the medical codes that are m

关键词： Codes Attention mechanisms Federated learning Medical services Predictive models Benchmark testing Sequential diagnosis

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A Hybrid Loss network for Localization of Image Manipulation 19th

A Hybrid Loss Network for Localization of Image Manipulation

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19th International Workshop on Digital Forensics and Watermarking, IWDW 2020

作者： Yin, Qilin Wang, Jinwei Luo, Xiangyang Nanjing University of Information Science and Technology and Engineering Research Center of Digital Forensics Ministry of Education Nanjing210044 China State Key Laboratory of Mathematical Engineering and Advanced Computing Henan450001 China Shanxi Key Laboratory of Network and System Security Xidian University Xi’an710071 China

ISBN: (纸本)9783030694487

With the development of information security, localization of image manipulations havs become a hot topic. In this paper, a hybrid loss network is proposed for the manipulated image forensics. First, the patch prediction module extracts corresponding features representing the discrepancy of the tampered region boundaries. Then, these features are constrained by the Pixel Normalization, thereby improving the classification performance for the tampered patches. Finally, multi-scale patch prediction masks and semantic information are fused to segment out the tampered regions. The experiments demonstrate the proposed model can achieve high performance. © 2021, Springer Nature Switzerland AG.

关键词： Pixels

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CSLP: Collaborative Solution to Long-Tail Problem and Popularity Bias in Sequential Recommendation

CSLP: Collaborative Solution to Long-Tail Problem and Popula...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yan Huang Zhenyu Yang Wenyue Hu Baojie Xu Zhibo Zhang The Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Sequential Recommender Systems (SRS), leveraging the temporal information from users' behaviors, have noticeably improved user experience against traditional systems. However, these behaviors often follow long-tail distribution, making the systems biased towards popular items (i.e., popularity bias). Moreover, popularity bias would amplify the neglect of long-tail recommendations, thereby sharpening the long-tail problem. Previous researches usually address these challenges independently, focusing on reducing the over-recommendation of popular items or enhancing the representation quality of tail items. Indeed, it is possible to incorporate their merits to achieve the best of both worlds. Thus, we propose a novel and unified framework, named Collaborative Solution to Long tailed problem and Popularity bias (CSLP), to tackle both the long-tail problem and popularity bias simultaneously. To achieve this, we first introduce a representation enhancement module featuring dual generators to enhance user and item representations, particularly for those in the tail. On the other hand, a debiasing module incorporating an Inverse Propensity Score (IPS) with a clipping strategy is introduced to further alleviate the popularity bias. Specifically, this clipping strategy demonstrates a clear decrease in the original IPS method's variance, effectively improving the recommendation for stability and accuracy. Experiments on three widely-used datasets show CSLP's effectiveness in solving both issues. CSLP surpasses all baselines (traditional, popularity bias, and long-tail problem) in overall performance, significantly enhancing recommendation accuracy for both tail users and items, and achieving a more balanced ratio of recommendations between popular and tail items. Code is available at https://***/Echohuangyan/CSLP.

关键词： Hands Heavily-tailed distribution Accuracy Collaboration Tail Linear programming Generators User experience IP networks Recommender systems

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