检索结果-内蒙古大学图书馆

JDC-GCN: joint diversity and consistency graph convolutional network

Neural Computing and Applications 2025年 1-17页

作者： Lin, Renjie Yao, Jie Wang, Shiping Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China

In real-world scenarios, multi-view data comprises heterogeneous features, with each feature corresponding to a specific view. The objective of multi-view semi-supervised classification is to enhance classification performance by leveraging the inherent complementary and consistent information present within diverse views. Nevertheless, many existing frameworks primarily focus on assigning suitable weights to different views while neglecting the importance of consistent information. In this paper, a multi-view semi-supervised classification framework called joint diversity and consistency graph convolutional network (JDC-GCN) is proposed. Firstly, the structure of graph convolutional network is introduced to the multi-view semi-supervised classification, capable of propagating the label information over the topological structure of multi-view data. Secondly, the proposed JDC-GCN captures the complementary and consistent information from multiple views through two indispensable sub-modules, Diversity-GCN and Consistency-GCN, respectively. Finally, the attention mechanism is leveraged to dynamically adjust the weights of various views, allowing us to measure the significance of heterogeneous features and the consistent graph without introducing additional parameters. Comprehensive experiments on eight multi-view datasets are conducted to validate the effectiveness of the JDC-GCN algorithm. The results show that the proposed method exhibits superior classification performance compared to other state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

DM-FedMF: A Recommendation Model of Federated Matrix Factorization with Detection Mechanism

引用

IEEE Transactions on network Science and Engineering 2025年

作者： Zheng, Xiaoyao Jia, Xianmin Cheng, Xiongchao He, Wenxuan Sun, Liping Guo, Liangmin Yu, Qingying Luo, Yonglong Anhui Normal University Anhui Provincial Key Laboratory of Network and Information Security School of Computer and Information Wuhu China

Items are recommended to users by the federated recommendation system while protecting user privacy, but there is a risk of the performance of the global model being seriously affected by malicious clients through the tampering of local data and model parameters. In this paper, a federated matrix factorization recommendation model with a detection mechanism(DM-FedMF) is proposed. The experimental analysis concludes that there is a gradient difference in item preference parameters between malicious and benign clients. Accordingly, an objective function is designed to measure item preference differences as a means of identifying malicious clients on the server. Secondly, a malicious client reporting mechanism is proposed to count the reported frequency of all clients and set a threshold. Based on the number of honest clients, the list of attackers is updated. Finally, the malicious client is detected and eliminated based on the list of attackers. The other three defense algorithms are compared with two public datasets in this paper. The experimental results show that the detection mechanism can effectively defend against data poisoning attacks, category attacks, noise attacks, and sign flipping attacks, and the performance of the model's recommendations is better than that achieved by applying other defense methods. © 2025 IEEE. All rights reserved.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Mamba-based spatial-spectral fusion network for hyperspectral unmixing

引用

Journal of King Saud University - computer and information Sciences 2025年第3期37卷 1-24页

作者： Gan, Yuquan Wei, Jingtao Xu, Mengmeng School of Telecommunication and Information Engineering Xi’an University of Posts & Telecommunications Xi’an 710121 China Xi’ an Key Laboratory of Image Processing Technology and Applications for Public Security Xi’an University of Posts & Telecommunications Xi’an 710121 China Shaanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts & Telecommunications Xi’an 710121 China

Hyperspectral unmixing (HU) is a critical technique in hyperspectral image (HSI) analysis, aimed at decomposing mixed pixels into a set of spectral signatures (endmembers) and their corresponding abundance values. Recently, the Mamba model has gained significant attention for its exceptional performance in natural language processing and has been extended to vision research. With its strong capability for long-range modeling and linear computational complexity, Mamba demonstrates substantial potential in hyperspectral image processing. However, due to the intrinsic requirement of HU tasks for comprehensive integration of spatial and spectral information, challenges remain in effectively leveraging Mamba for hyperspectral representation. To address these issues, we propose a novel Mamba-based spatial-spectral fusion network for hyperspectral unmixing (Mamba-SSFN). This network introduces a fusion mechanism to jointly learn spectral and spatial feature representations, enabling more efficient extraction of critical HSI features. Specifically, in the spatial feature extraction module, we integrate multi-scale analysis with the Mamba module, enabling the capturing of both local and global spatial information. In the spectral feature extraction module, the Mamba module is employed in a grouped manner to process spectral vectors, exploring the correlations among different spectral groups. Finally, an effective fusion mechanism is implemented to integrate spatial and spectral *** results demonstrate that Mamba-SSFN achieves outstanding performance across multiple benchmark datasets, significantly surpassing existing state-of-the-art methods in terms of unmixing accuracy, model robustness, and computational efficiency. © The Author(s) 2025.

关键词： Fusion mechanism Hyperspectral image Hyperspectral unmixing Mamba model Multi-Scale analysis

来源：评论

学校读者我要写书评

暂无评论

An Efficient and Secure Data Audit Scheme for Cloud-Based EHRs with Recoverable and Batch Auditing

引用

computers, Materials & Continua 2025年第4期83卷 1533-1553页

作者： Yuanhang Zhang Xu An Wang Weiwei Jiang Mingyu Zhou Xiaoxuan Xu Hao Liu College of Cryptography Engineering Engineering University of People’s Armed PoliceXi’an710086China Key Laboratory of Network and Information Security Engineering University of People’s Armed PoliceXi’an710086China School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing100876China

Cloud storage,a core component of cloud computing,plays a vital role in the storage and management of *** Health Records(EHRs),which document users’health information,are typically stored on cloud ***,users’sensitive data would then become *** the event of data loss,cloud storage providers might conceal the fact that data has been compromised to protect their reputation and mitigate *** the integrity of data stored in the cloud remains a pressing issue that urgently needs to be *** this paper,we propose a data auditing scheme for cloud-based EHRs that incorporates recoverability and batch auditing,alongside a thorough security and performance *** scheme builds upon the indistinguishability-based privacy-preserving auditing approach proposed by Zhou et *** identify that this scheme is insecure and vulnerable to forgery attacks on data storage *** address these vulnerabilities,we enhanced the auditing process using masking techniques and designed new algorithms to strengthen *** also provide formal proof of the security of the signature algorithm and the auditing ***,our results show that our scheme effectively protects user privacy and is resilient against malicious *** results indicate that our scheme is not only secure and efficient but also supports batch auditing of cloud ***,when auditing 10,000 users,batch auditing reduces computational overhead by 101 s compared to normal auditing.

关键词： security cloud computing cloud storage recoverable batch auditing

来源：评论

学校读者我要写书评

暂无评论

Textual Data De-Privatization Scheme Based on Generative Adversarial networks 24th

Textual Data De-Privatization Scheme Based on Generative Adv...

引用

24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Du, Yanning Xu, Jinnan Zhang, Yaling Wang, Yichuan Wang, Zhoukai School of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

ISBN: (纸本)9789819615506

In many fields, such as healthcare, finance, and scientific research, data sharing and collaboration are critical to achieving better outcomes. However, the sharing of personal data often involves privacy risks, so privacy-preserving techniques are needed to ensure data security and privacy. The superior performance and flexibility of generative models in data representation have led to significant progress in the development of data privacy. This paper proposes a textual data de-privatization scheme based on generative adversarial networks that well combines generative adversarial networks with privacy protection. The generated de-privacy data retains the statistical properties of the original data and effectively removes sensitive information. Moreover, feature sorting and reshaping modules are introduced to enable the generator to better capture the relationships between features, thus improving the quality of synthetic data. In this paper, the utility of synthetic data is evaluated from three aspects, including similarity assessment, privacy assessment, and model utility assessment. Experimental results show that this method achieves a good trade-off in terms of textual data privacy protection and data quality maintenance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

LP2CR-IoT: Lightweight and Privacy-Preserving Cross-Modal Retrieval in IoT

引用

IEEE Internet of Things Journal 2025年第10期12卷 14812-14827页

作者： Li, Mingyue Zhu, Yuting Du, Ruizhong Jia, Chunfu Hebei University School of Cyber Security and Computer Key Laboratory of High Trusted Information System in Hebei Province Hebei Baoding071002 China Nankai University Department of Cyber Sciences Tianjin300350 China

As a pivotal link between visual and linguistic relationships, image-text cross-modal retrieval has received widespread attention. However, existing studies primarily focus on intricate machine learning models to enhance retrieval accuracy and overlook the critical aspect of privacy preservation for images and texts, rendering them unsuitable for lightweight IoT environments. To tackle these challenges, we propose LP2CR-IoT, a lightweight and privacy-preserving cross-modal retrieval scheme tailored to IoT environments. LP2CR-IoT employs knowledge distillation to train lightweight student models for extracting feature vectors from images and texts, subsequently embedding them into a unified semantic space. Significantly, we propose a new training metric (i.e., intramodal consistent contrast loss), which improves the retrieval accuracy by increasing the semantic consistency of the image and text in the common embedding space. Additionally, a novel quadtree index structure leveraging hybrid representation vectors is designed to effectively mitigate retrieval overhead, where feature vectors of images and texts alongside representation vectors are encrypted using a secure kNN algorithm based on LWE, enabling image-text matching in a large-scale ciphertext environment. Finally, we provide a detailed formal analysis to evaluate the security of LP2CR-IoT and validate its practicality through extensive experiments on three real-world datasets, namely, COCO, Flickr30k, and NUS-WIDE. © 2014 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Enhancing Adversarial Example Transferability via Regularized Constrained Feature Layer

引用

computers, Materials & Continua 2025年第4期83卷 157-175页

作者： Xiaoyin Yi Long Chen Jiacheng Huang Ning Yu Qian Huang School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing400065China Chongqing Key Laboratory of Public Big Data Security Technology Chongqing401420China School of Cyber Security and Information Law Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of EducationChongqing400065China Artificial Intelligence and Big Data College Chongqing Polytechnic University of Electronic TechnologyChongqing401331China

Transfer-based Adversarial Attacks(TAAs)can deceive a victim model even without prior *** is achieved by leveraging the property of adversarial *** is,when generated from a surrogate model,they retain their features if applied to other models due to their good ***,adversarial examples often exhibit overfitting,as they are tailored to exploit the particular architecture and feature representation of source ***,when attempting black-box transfer attacks on different target models,their effectiveness is *** solve this problem,this study proposes an approach based on a Regularized Constrained Feature Layer(RCFL).The proposed method first uses regularization constraints to attenuate the initial examples of low-frequency *** are then added to a pre-specified layer of the source model using the back-propagation technique,in order to modify the original adversarial ***,a regularized loss function is used to enhance the black-box transferability between different target *** proposed method is finally tested on the ImageNet,CIFAR-100,and Stanford Car datasets with various target models,The obtained results demonstrate that it achieves a significantly higher transfer-based adversarial attack success rate compared with baseline techniques.

关键词： Adversarial examples black-box transferability regularized constrained transfer-based adversarial attacks

来源：评论

学校读者我要写书评

暂无评论

Embedding Data in Bitcoin Transaction Values with Restricted Boltzmann Machine 2nd

Embedding Data in Bitcoin Transaction Values with Restrict...

引用

2nd International Conference on Data security and Privacy Protection, DSPP 2024

作者： An, Jiayi Tian, Haibo Guangdong Province Key Laboratory of Information Security Technology School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China

ISBN: (纸本)9789819785391

Constructing covert channels on blockchains has recently become a significant research focus. A major challenge lies in embedding data into blockchain transactions, while maintaining strong concealment. This study focuses on utilizing blockchain transaction values as a carrier, exploring methods to ensure that the distribution of values with embedded data mimics that of standard transactions. Initially, we gathered authentic blockchain transaction data, conducted a frequency analysis on the transaction values, and filtered out abnormal values to establish a baseline normal value dataset. Subsequently, we employed a Restricted Boltzmann Machine (RBM) to generate a simulated value dataset that mirrors the distribution of the normal dataset, facilitating the embedding of data within the simulated value dataset. By fine-tuning the number of bits embedded in the values, we achieved a value dataset with embedded data that closely resembles the normal distribution. We have calculated the Kullback-Leibler Divergence and the Kolmogorov-Smirnov Test between the value dataset with embedded data and the normal value dataset under various embedding scenarios, revealing that the changes to the original dataset are minimal. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Normal distribution

来源：评论

学校读者我要写书评

暂无评论

A Reversible Data Hiding in Encryption Domain for JPEG Image Based on Controllable Ciphertext Range of Paillier Homomorphic Encryption Algorithm 21st

A Reversible Data Hiding in Encryption Domain for JPEG Ima...

引用

21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Ma, Bin Zhao, Chunxin Ma, Ruihe Xian, Yongjin Wang, Chunpeng Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China School of Economics Jilin University Changchun130015 China

ISBN: (纸本)9789819601219

With the rapid advancement of cloud computing, significant security concerns regarding data storage have been continuously raised. When the encrypted images are stored in the cloud, reversible data hiding in encrypted domain is used to embed additional data to manage the storage resources in the cloud. Most of the current works are applied to uncompressed images rather than JPEG images. Therefore, this paper proposes a reversible data hiding algorithm for JPEG images based on an enhanced Paillier homomorphic encryption algorithm. In the proposed scheme, the AC coefficients are altered by using a ciphertext distribution constrained paillier homomorphic encryption algorithm, so as to reduce the size of the data-embedded encrypted image. Whereas, the DC coefficients are encrypted by using the one-dimensional tangent chaos encryption algorithm, thereby ensuring high security of the encrypted image. Moreover, according to the characteristics of the homomorphic encryption algorithm, the confidential data can be recovered from both the encryption domain and from the plain domain. Experimental results demonstrate that the ciphertext distribution-constrained Paillier homomorphic encryption algorithm obtains higher data embedding capacity and superior security in comparison with other advanced algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Encryption algorithms

来源：评论

学校读者我要写书评

暂无评论

Extracting High-Discriminative Features for Detecting Double JPEG Compression With the Same Quantization Matrix

引用

IEEE Transactions on Circuits and Systems for Video Technology 2025年第5期35卷 4727-4739页

作者： Li, Wenjie Li, Xiaolong Ni, Rongrong Zhao, Yao Tianjin University of Technology School of Computer Science and Engineering Tianjin300384 China Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China

Detecting double JPEG compression with the same quantization matrix is a crucial yet challenging task in image forensics. Existing methods often fail to accurately identify and fully exploit the differences between singly and doubly compressed images, resulting in unsatisfactory detection performance, especially for cases with low quality factors (QFs). To address this issue, a novel method is proposed to extract highly discriminative features for performance enhancement. First, we design a new error block classification method that categorizes error blocks into stable error blocks, rounding error blocks (REBs), and truncation error blocks (TEBs). This classification method enables more accurate identification of TEBs, which are the most discriminative blocks in error images for cases with low QFs. Then, based on the theoretical analysis of REBs and TEBs, an intrinsic variable that directly leads to the differences between two classes of images is derived, providing more essential characteristics for the detection. Finally, a number of 25-dimensional highly discriminative features are extracted from REBs, TEBs, and flat blocks. Experimental results demonstrate that the proposed method outperforms several state-of-the-art works, especially on images with low QFs. © 1991-2012 IEEE.

关键词： Image compression

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：