检索结果-内蒙古大学图书馆

Reversible Data Hiding Algorithm in Encrypted Images Based on Adaptive Median Edge Detection and Ciphertext-Policy Attribute-Based Encryption

引用

Computers, Materials & Continua 2024年第10期81卷 1123-1155页

作者： Zongbao Jiang Minqing Zhang Weina Dong Chao Jiang Fuqiang Di Key Laboratory of Network and Information Security of People’s Armed Police Chinese People’s Armed Police Force Engineering UniversityXi’an710086China

With the rapid advancement of cloud computing technology,reversible data hiding algorithms in encrypted images(RDH-EI)have developed into an important field of study concentrated on safeguarding privacy in distributed cloud ***,existing algorithms often suffer from low embedding capacities and are inadequate for complex data access *** address these challenges,this paper proposes a novel reversible data hiding algorithm in encrypted images based on adaptive median edge detection(AMED)and ciphertext-policy attributebased encryption(CP-ABE).This proposed algorithm enhances the conventional median edge detection(MED)by incorporating dynamic variables to improve pixel prediction *** carrier image is subsequently reconstructed using the Huffman coding *** image generation is then achieved by encrypting the image based on system user attributes and data access rights,with the hierarchical embedding of the group’s secret data seamlessly integrated during the encryption process using the CP-ABE ***,the encrypted image is transmitted to the data hider,enabling independent embedding of the secret data and resulting in the creation of the marked encrypted *** approach allows only the receiver to extract the authorized group’s secret data,thereby enabling fine-grained,controlled *** results indicate that,in contrast to current algorithms,the method introduced here considerably improves the embedding rate while preserving lossless image ***,the average maximum embedding rates for the(3,4)-threshold and(6,6)-threshold schemes reach 5.7853 bits per pixel(bpp)and 7.7781 bpp,respectively,across the BOSSbase,BOW-2,and USD ***,the algorithm facilitates permission-granting and joint-decryption ***,this paper conducts a comprehensive examination of the algorithm’s robustness using metrics such as image correlation,information entropy,and number of pix

关键词： Ciphertext-policy attribute-based encryption complex data access structure reversible data hiding large embedding space

来源：评论

学校读者我要写书评

暂无评论

Heterogeneous Dual-Dynamic Attention network for Modeling Mutual Interplay of Stocks

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3595-3606页

作者： Liu, Haotian Zhou, Yadong Zhou, Yuxun Hu, Bowen Xi'an Jiaotong University Ministry of Education Key Laboratory for Intelligent Networks and Network Security Faculty of Electronic and Information Engineering Xi'an710049 China

Modern quantitative finance and portfolio-based investment hinge on the dependence structure among financial instruments (like stocks) for return prediction, risk management, and hedging. Several attempts have been made to leverage recent advancements in graph neural networks (GNNs) to achieve effective modeling to capture stock interactions. However, existing work suffers from the homogeneity and stationary of the predefined graph structure, and their reliance on hard-coded correlation-based relationships neglects the mutual intervention of different instruments. In this study, we propose the heterogeneous dual-dynamic attention network (HD2AT) to address these challenges in modeling the mutual interplay among stocks, across four key dimensions: heterogeneity, causality, structural dynamics, and lead-lag dynamics. More specifically, our method uncovers the causal interdependencies among instruments, dynamically inferring the heterogeneous network structure from real-time trading activities. Subsequently, we identify the cross-temporal propagation of interventions by integrating interactions at the node, time, and graph levels. Extensive experiments on real-world trading data demonstrate the superiority of the proposed framework over various baselines in terms of both return prediction and back-testing profitability. © 2020 IEEE.

关键词： Commerce

来源：评论

学校读者我要写书评

暂无评论

Blockchain and signcryption enabled asynchronous federated learning framework in fog computing

引用

Digital Communications and networks 2025年第2期11卷 442-454页

作者： Zhou Zhou Youliang Tian Jinbo Xiong Changgen Peng Jing Li Nan Yang State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber SecurityFujian Normal UniversityFuzhou 350117China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic TechnologyGuilin 541004China

Federated learning combines with fog computing to transform data sharing into model sharing,which solves the issues of data isolation and privacy disclosure in fog ***,existing studies focus on centralized single-layer aggregation federated learning architecture,which lack the consideration of cross-domain and asynchronous robustness of federated learning,and rarely integrate verification mechanisms from the perspective of *** address the above challenges,we propose a Blockchain and Signcryption enabled Asynchronous Federated Learning(BSAFL)framework based on dual aggregation for cross-domain *** particular,we first design two types of signcryption schemes to secure the interaction and access control of collaborative learning between ***,we construct a differential privacy approach that adaptively adjusts privacy budgets to ensure data privacy and local models'availability of intra-domain ***,we propose an asynchronous aggregation solution that incorporates consensus verification and elastic participation using ***,security analysis demonstrates the security and privacy effectiveness of BSAFL,and the evaluation on real datasets further validates the high model accuracy and performance of BSAFL.

关键词： Blockchain Signcryption Federated learning Asynchronous Fog computing

来源：评论

学校读者我要写书评

暂无评论

Minimizing CNOT-count in quantum circuit of the extended Shor’s algorithm for ECDLP

引用

Cybersecurity 2025年第1期8卷 153-179页

作者： Xia Liu Huan Yang Li Yang State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of SciencesBeijingChina School of Cyber Security University of Chinese Academy of SciencesBeijingChina Beijing Youzhuju Network Technology Co. Ltd.BeijingChina

The elliptic curve discrete logarithm problem(ECDLP)is a popular choice for cryptosystems due to its high level of ***,with the advent of the extended Shor’s algorithm,there is concern that ECDLP may soon be *** the algorithm does ofer hope in solving ECDLP,it is still uncertain whether it can pose a real threat in *** the perspective of the quantum circuits of the algorithm,this paper analyzes the feasibility of cracking ECDLP using an ion trap quantum computer with improved quantum circuits for the extended Shor’s *** give precise quantum circuits for extended Shor’s algorithm to calculate discrete logarithms on elliptic curves over prime felds,including modular subtraction,three diferent modular multiplication,and modular ***,we incorporate and improve upon windowed arithmetic in the circuits to reduce the *** previous studies mostly focused on minimizing the number of qubits or the depth of the circuit,we focus on minimizing the number of CNOT gates in the circuit,which greatly afects the running time of the algorithm on an ion trap quantum ***,we begin by presenting implementations of basic arithmetic operations with the lowest known CNOT-counts,along with improved constructions for modular inverse,point addition,and windowed ***,we precisely estimate that,to execute the extended Shor’s algorithm with the improved circuits to factor an n-bit integer,the CNOT-count required is1237n^(3)/log n+2n^(2)+***,we analyze the running time and feasibility of the extended Shor’s algorithm on an ion trap quantum computer.

关键词： Elliptic curve discrete logarithm problem Extended Shor’s algorithm Quantum circuits Ion trap quantum computer

来源：评论

学校读者我要写书评

暂无评论

Full-round impossible diferential attack on shadow block cipher

引用

Cybersecurity 2025年第1期8卷 247-261页

作者： Yuting Liu Yongqiang Li Huiqin Chen Mingsheng Wang State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of SciencesBeijingChina School of Cyber Security University of Chinese Academy of SciencesBeijingChina

Lightweight block ciphers are the essential encryption algorithm for devices with limited *** goal is to ensure the security of data transmission through resource-constrained *** diferential cryptanalysis is one of the most efective cryptanalysis on block ciphers,and assessing the ability of resisting this attack is a basic design *** is a lightweight block cipher proposed by Guo et al.(IEEE Internet Things J 8(16):13014-13023,2021).It utilizes a combination of ARX operations and generalized Feistel structure to overcome the weakness of the traditional Feistel structure that only difuses half in one *** this paper,we focus on the differential property of Shadow and its security against impossible diferential ***,we use the SAT method to automatically search for a full-round impossible diferential distinguisher of ***,based on the experimental results,we prove that Shadow has a diferential property with probability 1 based on the propagation of the ***,we can obtain an impossible diferential distinguisher for an arbitrary number of rounds of ***,we perform a full key recovery attack on the full-round Shadow-32 and *** experimentally and theoretically,our results indicate that Shadow is critically fawed,and regardless of the security strength of the internal components and the number of rounds applied,the overall cipher remains vulnerable to impossible diferential cryptanalysis.

关键词： Lightweight block cipher Shadow Impossible diferential cryptanalysis SAT

来源：评论

学校读者我要写书评

暂无评论

Partial multi-label learning via label-specific feature corrections

引用

Science China(information Sciences) 2025年第3期68卷 95-109页

作者： Jun-Yi HANG Min-Ling ZHANG School of Computer Science and Engineering Southeast University Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of Education

Partial multi-label learning（PML） allows learning from rich-semantic objects with inaccurate annotations, where a set of candidate labels are assigned to each training example but only some of them are valid. Existing approaches rely on disambiguation to tackle the PML problem, which aims to correct noisy candidate labels by recovering the ground-truth labeling information ahead of prediction model induction. However, this dominant strategy might be suboptimal as it usually needs extra assumptions that cannot be fully satisfied in real-world scenarios. Instead of label correction, we investigate another strategy to tackle the PML problem, where the potential ambiguity in PML data is eliminated by correcting instance features in a label-specific manner. Accordingly, a simple yet effective approach named PASE, i.e., partial multi-label learning via label-specific feature corrections, is proposed. Under a meta-learning framework, PASElearns to exert label-specific feature corrections so that potential ambiguity specific to each class label can be eliminated and the desired prediction model can be induced on these corrected instance features with the provided candidate labels. Comprehensive experiments on a wide range of synthetic and real-world data sets validate the effectiveness of the proposed approach.

关键词： machine learning multi-label learning partial multi-label learning label-specific features feature correction

来源：评论

学校读者我要写书评

暂无评论

Multimodal Cross-Attention Mechanism-Based Algorithm for Elderly Behavior Monitoring and Recognition

引用

Chinese Journal of Electronics 2025年第1期34卷 309-321页

作者： Hao Liu Zhiquan Feng Qingbei Guo School of Information Science and Engineering Unicersity of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Unicersity of Jinan

In contrast to the general population, behavior recognition among the elderly poses increased specificity and difficulty, rendering the reliability and usability aspects of safety monitoring systems for the elderly more challenging. Hence, this study proposes a multi-modal perception-based solution for an elderly safety monitoring recognition system. The proposed approach introduces a recognition algorithm based on multi-modal cross-attention mechanism, innovatively incorporating complex information such as scene context and voice to achieve more accurate behavior recognition. By fusing four modalities, namely image, skeleton, sensor data, and audio, we further enhance the accuracy of recognition. Additionally, we introduce a novel human-robot interaction mode, where the system associates directly recognized intentions with robotic actions without explicit commands, delivering a more natural and efficient elderly assistance paradigm. This mode not only elevates the level of safety monitoring for the elderly but also facilitates a more natural and efficient caregiving approach. Experimental results demonstrate significant improvement in recognition accuracy for 11 typical elderly behaviors compared to existing methods.

关键词： Privacy Accuracy Image recognition Speech recognition Robot sensing systems Skeleton Safety Reliability Older adults Monitoring

来源：评论

学校读者我要写书评

暂无评论

DLP:towards active defense against backdoor attacks with decoupled learning process

引用

Cybersecurity 2024年第1期7卷 122-134页

作者： Zonghao Ying Bin Wu State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of SciencesBeijingChina School of Cyber Security University of Chinese Academy of SciencesBeijingChina

Deep learning models are well known to be susceptible to backdoor attack,where the attacker only needs to provide a tampered dataset on which the triggers are *** trained on the dataset will passively implant the backdoor,and triggers on the input can mislead the models during *** study shows that the model shows different learning behaviors in clean and poisoned subsets during *** on this observation,we propose a general training pipeline to defend against backdoor attacks *** models can be trained from the unreli-able dataset by decoupling the learning process into three stages,i.e.,supervised learning,active unlearning,and active semi-supervised *** effectiveness of our approach has been shown in numerous experiments across various backdoor attacks and datasets.

关键词： Deep learning Backdoor attack Active defense

来源：评论

学校读者我要写书评

暂无评论

Fairness is essential for robustness:fair adversarial training by identifying and augmenting hard examples

引用

Frontiers of Computer Science 2025年第3期19卷 1-13页

作者： Ningping MOU Xinli YUE Lingchen ZHAO Qian WANG Key Laboratory of Aerospace Information Security and Trusted Computing(Ministry of Education) School of Cyber Science and EngineeringWuhan UniversityWuhan 430072China

Adversarial training has been widely considered the most effective defense against adversarial ***,recent studies have demonstrated that a large discrepancy exists in the class-wise robustness of adversarial training,leading to two potential issues:firstly,the overall robustness of a model is compromised due to the weakest class;and secondly,ethical concerns arising from unequal protection and biases,where certain societal demographic groups receive less robustness in defense *** these issues,solutions to address the discrepancy remain largely *** this paper,we advance beyond existing methods that focus on class-level *** investigation reveals that hard examples,identified by higher cross-entropy values,can provide more fine-grained information about the ***,we find that enhancing the diversity of hard examples can effectively reduce the robustness gap between *** by these observations,we propose Fair Adversarial Training(FairAT)to mitigate the discrepancy of class-wise *** experiments on various benchmark datasets and adversarial attacks demonstrate that FairAT outperforms state-of-the-art methods in terms of both overall robustness and *** a WRN-28-10 model trained on CIFAR10,FairAT improves the average and worst-class robustness by 2.13%and 4.50%,respectively.

关键词： robust fairness adversarial training hard example data augmentation

来源：评论

学校读者我要写书评

暂无评论

A Model for Detecting Fake News by Integrating Domain-Specific Emotional and Semantic Features

引用

Computers, Materials & Continua 2024年第8期80卷 2161-2179页

作者： Wen Jiang Mingshu Zhang Xu’an Wang Wei Bin Xiong Zhang Kelan Ren Facheng Yan 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 Key Laboratory of CTC&Information Engineering Engineering University of People’s Armed PoliceMinistry of EducationXi’an710086China

With the rapid spread of Internet information and the spread of fake news,the detection of fake news becomes more and more *** detection methods often rely on a single emotional or semantic feature to identify fake news,but these methods have limitations when dealing with news in specific *** order to solve the problem of weak feature correlation between data from different domains,a model for detecting fake news by integrating domain-specific emotional and semantic features is *** method makes full use of the attention mechanism,grasps the correlation between different features,and effectively improves the effect of feature *** algorithm first extracts the semantic features of news text through the Bi-LSTM(Bidirectional Long Short-Term Memory)layer to capture the contextual relevance of news ***-BiLSTM is then used to extract emotional features and predict the probability of positive and negative emotions in the *** then uses domain features as an enhancement feature and attention mechanism to fully capture more fine-grained emotional features associated with that ***,the fusion features are taken as the input of the fake news detection classifier,combined with the multi-task representation of information,and the MLP and Softmax functions are used for *** experimental results show that on the Chinese dataset Weibo21,the F1 value of this model is 0.958,4.9% higher than that of the sub-optimal model;on the English dataset FakeNewsNet,the F1 value of the detection result of this model is 0.845,1.8% higher than that of the sub-optimal model,which is advanced and feasible.

关键词： Fake news detection domain-related emotional features semantic features feature fusion

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：