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arXiv 2019年

作者： Zhu, Li Xie, Zihao Liu, Liman Tao, Bo Tao, Wenbing National Key Laboratory of Science and Technology on Multi-spectral Information Processing School of Artifcial Intelligence and Automation Huazhong University of Science and Technology Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057 China State Key Laboratory of Digital Manufacturing Equipment and Technology School of Mechanical Science and Engineering Huazhong University of Science and Technology WuhanHubei430074 China School of Biomedical Engineering South-Central University for Nationalities Wuhan430074 China

Region Proposal Network (RPN) is the cornerstone of two-stage object detectors, it generates a sparse set of object proposals and alleviates the extrem foreground-background class imbalance problem during training. However, we find that the potential of the detector has not been fully exploited due to the IoU distribution imbalance and inadequate quantity of the training samples generated by RPN. With the increasing intersection over union (IoU), the exponentially smaller numbers of positive samples would lead to the distribution skewed towards lower IoUs, which hinders the optimization of detector at high IoU levels. In this paper, to break through the limitations of RPN, we propose IoU-Uniform R-CNN, a simple but effective method that directly generates training samples with uniform IoU distribution for the regression branch as well as the IoU prediction branch. Besides, we improve the performance of IoU prediction branch by eliminating the feature offsets of RoIs at inference, which helps the NMS procedure by preserving accurately localized bounding box. Extensive experiments on the PASCAL VOC and MS COCO dataset show the effectiveness of our method, as well as its compatibility and adaptivity to many object detection architectures. The code is made publicly availab.e at https://***/zl1994/IoU-Uniform-R-CNN. Copyright © 2019, The Authors. All rights reserved.

关键词： Object detection

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Towards real-time eyeblink detection in the wild: Dataset, theory and practices

arXiv

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arXiv 2019年

作者： Hu, Guilei Xiao, Yang Cao, Zhiguo Meng, Lubin Fang, Zhiwen Zhou, Joey Tianyi Yuan, Junsong National Key Laboratory of Science and Technology on Multi-Spectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Guangdong Provincial Key Laboratory of Medical Image Processing School of Biomedical Engineering Southern Medical University China School of Energy and Mechanicalelectronic Engineering Hunan University of Humanities Science and Technology China Institute of High Performance Computing A∗STAR Singapore Computer Science and Engineering Department of University at Buffalo State University of New York United States

Effective and real-time eyeblink detection is of widerange applications, such as deception detection, drive fatigue detection, face anti-spoofing. Despite previous efforts, most of existing focus on addressing the eyeblink detection problem under constrained indoor conditions with relative consistent subject and environment setup. Nevertheless, towards practical applications, eyeblink detection in the wild is highly preferred, and of greater challenges. In this paper, we shed the light to this research topic. A lab.lled eyeblink in the wild dataset (i.e., HUST-LEBW) of 673 eyeblink video samples (i.e., 381 positives, and 292 negatives) is first established. These samples are captured from the unconstrained movies, with the dramatic variation on face attribute, head pose, illumination condition, imaging configuration, etc. Then, we formulate eyeblink detection task as a binary spatial-temporal pattern recognition problem. After locating and tracking human eyes using SeetaFace engine and KCF (Kernelized Correlation Filters) tracker respectively, a modified LSTM model able to capture the multi-scale temporal information is proposed to verify eyeblink. A feature extraction approach that reveals the appearance and motion characteristics simultaneously is also proposed. The experiments on HUSTLEBW reveal the superiority and efficiency of our approach. The comparisons with the existing state-of-the-art methods validate the advantages of our manner for eyeblink detection in the wild. Copyright © 2019, The Authors. All rights reserved.

关键词： Long short-term memory

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The minority matters: a diversity-promoting collab.rative metric learning algorithm 22

The minority matters: a diversity-promoting collaborative me...

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Proceedings of the 36th International Conference on Neural information processing Systems

作者： Shilong Bao Qianqian Xu Zhiyong Yang Yuan He Xiaochun Cao Qingming Huang State Key Laboratory of Information Security Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS School of Computer Science and Tech. University of Chinese Academy of Sciences Alibaba Group School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Laboratory of Big Data Mining and Knowledge Management CAS and Peng Cheng Laboratory

ISBN: (纸本)9781713871088

Collab.rative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collab.rative Filtering. Following the convention of RS, existing methods exploit unique user representation in their model design. This paper focuses on a challenging scenario where a user has multiple categories of interests. Under this setting, we argue that the unique user representation might induce preference bias, especially when the item category distribution is imbalanced. To address this issue, we propose a novel method called Diversity-Promoting Collab.rative Metric Learning (DPCML), with the hope of considering the commonly ignored minority interest of the user. The key idea behind DPCML is to include a multiple set of representations for each user in the system. Based on this embedding paradigm, user preference toward an item is aggregated from different embeddings by taking the minimum item-user distance among the user embedding set. Furthermore, we observe that the diversity of the embeddings for the same user also plays an essential role in the model. To this end, we propose a Diversity Control Regularization Scheme (DCRS) to accommodate the multi-vector representation strategy better. Theoretically, we show that DPCML could generalize well to unseen test data by tackling the challenge of the annoying operation that comes from the minimum value. Experiments over a range of benchmark datasets speak to the efficacy of DPCML.

关键词：

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HF-Mid: A Hybrid Framework of Network Intrusion Detection for multi-type and Imbalanced Data

HF-Mid: A Hybrid Framework of Network Intrusion Detection fo...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Weidong Zhou Tianbo Wang Guotao Huang Xiaopeng Liang Chunhe Xia Xiaojian Li Beijing Key Lab. of Network Technology Beihang University Beijing China School of Cyber Science and Technology Beihang University Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing Guangxi Normal University Guilin China College of Computer Science and Information Technology Guangxi Normal University Guilin China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

The data-driven deep learning methods have brought significant progress and potential to intrusion detection. However, there are two thorny problems caused by the characteristics of intrusion data: "multi-type features" and "data imbalance". The former means that forcefully and improperly transforming intrusion features from distinct metric spaces can result in semantic loss and noise. The latter indicates that the intrusion data is imbalanced in quantity and quality due to its complex spatial distribution. We propose a Hybrid Framework for multi-type and Imbalance Data (HF-Mid) to address the above two problems. Firstly, we divide the intrusion features into equivalent and non-equivalent groups, and then embed them sequentially using Supervised Paragraph Vector-Distributed Memory (SPV-DM), which excels at modeling co-occurrence relationships, and Deep Neural Network (DNN), which is suitable for modeling non-linear relationships, thereby solving the "multitype features" problem. Secondly, we adopt a low-noise collective matrix factorization (CMF) model to fuse the two obtained features for dimensionality reduction. Finally, we employ a multiple classifier to detect intrusion. During the classifier training stage, we design a genetic algorithm-based proportional sampling method to select high-quality samples in each training batch. thus addressing the "data imbalance" problem. The experimental results demonstrate the proposed framework exhibits an overall improvement of 5.9% and 1.5% in terms of accuracy and false positive rate on average, respectively.

关键词： Training Privacy Fuses Noise Semantics Artificial neural networks Feature extraction

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HL-DPoS: An Enhanced Anti-Long-Range Attack DPoS Algorithm

arXiv

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arXiv 2023年

作者： Li, Yang Xia, Chunhe Li, Chunyan Zhao, Yuan Chen, Chen Wang, Tianbo School of Computer Science and Engineering Beihang University Beijing China Beijing Key Lab. of Network Technology Beihang University Beijing China Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing Guangxi Normal University Guilin China School of Cyber Science and Technology Beihang University Beijing China

The consensus algorithm is crucial in blockchain for ensuring the validity and security of transactions across the decentralized network. However, achieving consensus among nodes and packaging blocks in blockchain networks is a complex task that requires efficient and secure consensus algorithms. The DPoS consensus algorithm has emerged as a popular choice due to its fast transaction processing and high throughput. Despite these advantages, the algorithm still suffers from weaknesses such as centralization and vulnerability to long-range attacks, which can compromise the integrity of the blockchain network. To combat these problems, we developed an Enhanced Anti-Long-Range Attack DPoS algorithm (HL-DPoS). First, we split nodes into pieces to reduce centralization issues while giving witness nodes the power to report and benefit from malicious node's reports, maintaining high efficiency and high security. Second, we propose a validation method in HL-DPoS that compares consensuses transactions with the longest chain to detect long-range attacks. Algorithm analysis and simulation experiment results demonstrate that our HL-DPoS consensus algorithm improves security while achieving better consensus performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Blockchain

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FedDLM: A Fine-Grained Assessment Scheme for Risk of Sensitive information Leakage in Federated Learning-based Android Malware Classifier

FedDLM: A Fine-Grained Assessment Scheme for Risk of Sensiti...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Changnan Jiang Chunhe Xia Chen Chen Huacheng Li Tianbo Wang Xiaojian Li Beijing Key Lab. of Network Technology Beihang University Beijing China Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin China School of Cyber Science and Technology Beihang University Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China College of Computer Science and Information Technology Guangxi Normal University Guilin China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

In the traditional centralized Android malware classification framework, privacy concerns arise as it requires collecting users’ app samples containing sensitive information directly. To address this problem, new classification frameworks based on Federated Learning (FL) have emerged for privacy preservation. However, research shows that these frameworks still face risks of indirect information leakage due to adversary inference. Unfortunately, existing research lacks an effective assessment of the extent and location of this leakage risk. To bridge the gap, we propose the FedDLM, which provides a fine-grained assessment of the risk of sensitive information leakage in an FL-based Android malware classifier. FedDLM estimates attackers’ theoretical maximum inference ability from the information theory perspective to gauge the degree of leakage risk in the classifier effectively. It precisely identifies critical positions in the shared gradient where the leakage risk exists by utilizing characteristics of class activation in classifiers. Through extensive experiments on the Androzoo dataset, FedDLM demonstrates its superior effectiveness and precision compared to baseline methods in evaluating the risk of sensitive information leakage. The evaluation results provide valuable insights into information leakage problems in classifiers and targeted privacy protection methods.

关键词： Meters Privacy Federated learning information leakage Malware Security Risk management

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FedDADP: A Privacy-Risk-Adaptive Differential Privacy Protection Method for Federated Android Malware Classifier

FedDADP: A Privacy-Risk-Adaptive Differential Privacy Protec...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Changnan Jiang Chunhe Xia Mengyao Liu Rui Fang Pengfei Li Huacheng Li Tianbo Wang Beijing Key Lab. of Network Technology Beihang University Beijing China Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin China School of Cyber Science and Technology Beihang University Beijing China SNationa Innovation Center of Intelligent and Connected Vehicles Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The federated Android malware classifier has attracted much attention owing to its advantages of privacy protection and multi-party joint modeling. However, the research indicates that the gradient transmitted within the federated classifier still encodes the user's sensitive information, exposing it to indirect privacy inference threats from curious servers. Differential privacy is a recognized and effective way to address this privacy breach threat by adding noise to the user's model parameters to limit the attacker's inference of sensitive information. However, the protection effect of existing differential privacy methods is at the cost of significantly reducing the model's classification accuracy, and it cannot be reasonably balanced. To address this challenge, we propose a privacy protection method, FedDADP. FedDADP performs adaptive, lightweight privacy configuration in its training time dimension and model space dimension according to the privacy risk distribution law in the federated Android malware classifier to protect users' privacy while maintaining the model's utility. Numerous experiments on the Androzoo dataset and multiple baseline classifiers show that FedDADP protects users' sensitive information better (7% more effectiveness against adversaries' inference) than baseline differential privacy methods and achieves better model utility (classification accuracy improves by about 8%) with the same privacy budget.

关键词： Training Privacy Adaptation models Differential privacy Accuracy Privacy breach Malware

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Corrigendum to “Low-light image enhancement with knowledge distillation” [Neurocomputing 518 (2023) 332–343]

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Neurocomputing 2023年 523卷 144-144页

作者： Ziwen Li Yuehuan Wang Jinpu Zhang National Key Lab of Science and Technology on Multi-spectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China

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FedDRC: A Robust Federated Learning-based Android Malware Classifier under Heterogeneous Distribution

FedDRC: A Robust Federated Learning-based Android Malware Cl...

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International Conference on Computer Supported Cooperative Work in Design

作者： Changnan Jiang Chunhe Xia Mengyao Liu Chen Chen Huacheng Li Tianbo Wang Pengfei Li Beijing Key Lab. of Network Technology Beihang University Beijing China Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin China School of Cyber Science and Technology Beihang University Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China SNationa Innovation Center of Intelligent and Connected Vehicles Beijing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In the traditional centralized Android malware classification framework, privacy concerns exist due to collected users’ apps containing sensitive information. A new classification framework based on Federated Learning (FL) has emerged to protect privacy. However, significant spatiotemporal heterogeneity exists in the distribution of Android malware samples in different clients. It presents a huge challenge to existing FL schemes, as trained local models differ significantly, resulting in slower model convergence and lower classification accuracy. To bridge this gap, we propose FedDRC, a robust FL-based Android malware classifier. First, we design a functional semantic embedding mechanism of API features, FSEM, using word embedding to improve the robustness of the model to the time heterogeneity of the client’s samples. Secondly, we use the idea of information Bottleneck (IB) and transfer learning to design a robust local model, PAMIB, to deal with the model degradation caused by the space heterogeneity of the distribution of client samples. Extensive experiments on the Androzoo dataset show that FedDRC has the best robustness for Android malware classification tasks in various heterogeneity distribution settings: fastest convergence and best classification accuracy.

关键词： Degradation Privacy Accuracy Federated learning Transfer learning Semantics Malware

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iSplit LBI: Individualized partial ranking with ties via split LBI

arXiv

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arXiv 2019年

作者： Xu, Qianqian Sun, Xinwei Yang, Zhiyong Cao, Xiaochun Huang, Qingming Yao, Yuan Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS Microsoft Research Asia State Key Laboratory of Information Security Institute of Information Engineering CAS School of Cyber Security University of Chinese Academy of Sciences School of Computer Science and Tech. University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management CAS Peng Cheng Laboratory Department of Mathematics Hong Kong University of Science and Technology Hong Kong

Due to the inherent uncertainty of data, the problem of predicting partial ranking from pairwise comparison data with ties has attracted increasing interest in recent years. However, in real-world scenarios, different individuals often hold distinct preferences. It might be misleading to merely look at a global partial ranking while ignoring personal diversity. In this paper, instead of learning a global ranking which is agreed with the consensus, we pursue the tie-aware partial ranking from an individualized perspective. Particularly, we formulate a unified framework which not only can be used for individualized partial ranking prediction, but also be helpful for abnormal user selection. This is realized by a variable splitting-based algorithm called iSplitLBI. Specifically, our algorithm generates a sequence of estimations with a regularization path, where both the hyperparameters and model parameters are updated. At each step of the path, the parameters can be decomposed into three orthogonal parts, namely, abnormal signals, personalized signals and random noise. The abnormal signals can serve the purpose of abnormal user selection, while the abnormal signals and personalized signals together are mainly responsible for individual partial ranking prediction. Extensive experiments on simulated and real-world datasets demonstrate that our new approach significantly outperforms state-of-the-art alternatives. The code is now availiable at https://***/qianqianxu010/ NeurIPS2019-iSplitLBI. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

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