检索结果-内蒙古大学图书馆

HTOTP: Honey Time-Based One-Time Passwords

IEEE Transactions on information Forensics and Security 2025年 20卷 4438-4453页

作者： Ding, Zixuan Wang, Ding Nankai University College of Cryptology and Cyber Science Key Laboratory of Data and Intelligent System Security Ministry of Education Tianjin300350 China Chinese Academy of Sciences Key Laboratory of Cyberspace Security Defense Institute of Information Engineering Beijing100085 China

One-Time Passwords (OTPs) play a crucial role in Two-Factor Authentication (2FA) and Multi-Factor Authentication (MFA) by adding an additional layer of security. OTPs effectively reduce the risk of static passwords being intercepted and reused. Nevertheless, both academic schemes and industrial solutions face security threats stemming from server/device compromises and OTP factor forgery. Chain-based asymmetric OTP schemes are a promising approach to addressing the problem of server compromise but still face threats from device compromise and pre-generated chain leakage. We emphasize that since devices directly store the OTP seed, OTP authentication is essentially equivalent to verifying device possession. This means that in existing OTP schemes, OTP forgery and device compromise remain prevalent and difficult to overcome. In this work, we propose a brand new scheme to address OTP factor forgery and server/device compromises. For the first time, our scheme constructs a tightly coupled architecture between the password factor and the OTP factor. The OTP seed is derived from a password and a device-stored salt, preventing OTP seed extraction and OTP forgery even in the event of a device compromise. Through the integration of "honeywords" with the tightly coupled OTP architecture, the server stores decoy OTP seeds generated by decoy passwords, providing resistance against server compromises and partial password guessing from devices. We conduct a comprehensive evaluation of our OTP schemes. The computational overhead is correlated with the number of honeywords, and with the recommended set size of 20, the total verification overhead is approximately 0.24~ms . Additionally, we propose formal security properties and application metrics, and rigorously prove our scheme’s resistance against server/device compromise attacks and guessing attacks. Our scheme is the first to achieve comprehensive OTP security with low overhead. © 2005-2012 IEEE.

关键词： Authentication

来源：评论

学校读者我要写书评

暂无评论

Learning with Open-world Noisy data via Class-independent Margin in Dual Representation Space

arXiv

引用

arXiv 2025年

作者： Pan, Linchao Gao, Can Zhou, Jie Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China

Learning with Noisy Labels (LNL) aims to improve the model generalization when facing data with noisy labels, and existing methods generally assume that noisy labels come from known classes, called closed-set noise. However, in real-world scenarios, noisy labels from similar unknown classes, i.e., open-set noise, may occur during the training and inference stage. Such open-world noisy labels may significantly impact the performance of LNL methods. In this study, we propose a novel dual-space joint learning method to robustly handle the open-world noise. To mitigate model overfitting on closed-set and open-set noises, a dual representation space is constructed by two networks. One is a projection network that learns shared representations in the prototype space, while the other is a One-Vs-All (OVA) network that makes predictions using unique semantic representations in the class-independent space. Then, bi-level contrastive learning and consistency regularization are introduced in two spaces to enhance the detection capability for data with unknown classes. To benefit from the memorization effects across different types of samples, class-independent margin criteria are designed for sample identification, which selects clean samples, weights closed-set noise, and filters open-set noise effectively. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods and achieves an average accuracy improvement of 4.55% and an AUROC improvement of 6.17% on CIFAR80N. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Big-Moe: Bypassing Isolated Gating For Generalized Multimodal Face Anti-Spoofing

Big-Moe: Bypassing Isolated Gating For Generalized Multimoda...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yingjie Ma Zitong Yu Xun Lin Weicheng Xie Linlin Shen College of Computer Science and Software Engineering Shenzhen University Great Bay University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the domain of facial recognition security, multimodal Face Anti-Spoofing (FAS) is essential for countering presentation attacks. However, existing technologies encounter challenges due to modality biases and imbalances, as well as domain shifts. Our research introduces a Mixture of Experts (MoE) model to address these issues effectively. We identified three limitations in traditional MoE approaches to multimodal FAS: (1) Coarse-grained experts’ inability to capture nuanced spoofing indicators; (2) Gated networks’ susceptibility to input noise affecting decision-making; (3) MoE’s sensitivity to prompt tokens leading to overfitting with conventional learning methods. To mitigate these, we propose the Bypass Isolated Gating MoE (BIG-MoE) framework, featuring: (1) Fine-grained experts for enhanced detection of subtle spoofing cues; (2) An isolation gating mechanism to counteract input noise; (3) A novel differential convolutional prompt bypass enriching the gating network with critical local features, thereby improving perceptual capabilities. Extensive experiments on four benchmark datasets demonstrate significant generalization performance improvement in multimodal FAS task. The code is released at https://***/murInJ/BIG-MoE.

关键词： Convolutional codes Sensitivity Convolution Face recognition Noise Logic gates Feature extraction Security Speech processing Overfitting

来源：评论

学校读者我要写书评

暂无评论

EFIE: Efficient and Fine-Grained Image Encryption Algorithm With Permutation-Substitution Synchronization

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Xie, Dong Zhang, Guiting Yang, Menglin Chen, Fulong Wang, Taochun Hu, Peng Anhui Normal University Anhui Provincial Key Laboratory of Industrial Intelligence Data Security Anhui Engineering Research Centers of Medical Big Data Intelligent System School of Computer and Information Wuhu 241002 China

Currently, both pixel-level and bit-level image encryption algorithms demonstrate excellent visual performance. However, most high-security algorithms usually involve complex encryption steps, which increase the time complexity. In addition, some algorithms are challenging to adjust flexibly according to specific scenarios, making it difficult to achieve a balance between security and efficiency. In this paper, we propose an efficient and fine-grained image encryption algorithm, named EFIE. The main step of EFIE is the proposed permutation-substitution synchronization procedure, which is much more efficient compared to existing image encryption solutions. To our knowledge, we first introduce the concept of fine-grained encryption, which allows the encryptor to flexibly set the encryption unit (e.g., 1-bit, 2-bit, or 1-pixel). The smaller the length of the encryption unit, the longer the encryption time and the higher the level of security. The experimental results and various security analyses demonstrate that EFIE provides excellent encryption performance. When the encryption unit is 1 bit, encrypting a 512 × 512 color image requires only about 2.34 seconds. © 1975-2011 IEEE.

关键词： Fine-grained encryption Fisher-Yates scrambling algorithm Image encryption Permutation-substitution synchronization

来源：评论

学校读者我要写书评

暂无评论

An Encoder-Decoder Model Based On Spiking Neural Networks For Address Event Representation Object Recognition

引用

IEEE Transactions on Cognitive and Developmental systems 2025年

作者： Du, Sichun Zhu, Haodi Zhang, Yang Hong, Qinghui Hunan University College of Computer Science and Electronic Engineering Changsha418002 China Shenzhen University Computer Vision Institute School of Computer Science and Software Engineering National Engineering Laboratory for Big Data System Computing Technology Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

Address event representation (AER) object recognition task has attracted extensive attention in neuromorphic vision processing. The spike-based and event-driven computation inherent in the spiking neural network (SNN) provides an energy-saving solution for AER object recognition. However, SNN with spike timing dependent plasticity (STDP) learning rule has not achieved satisfying AER object recognition performance. This work proposes an SNN-based encoder-decoder model to improve the recognition performance of AER objects. An STDP-based locally connected spiking neural network (LC-SNN) is proposed as an encoder to extract rich spatiotemporal features from AER event flows more flexibly. After the encoder extracts and learns primary features, we propose a fully connected spiking neural network (FC-SNN) based on the reward-modulated spike-timing-dependent plasticity (R-STDP) learning rule as a decoder to learn higher-level features for classification. In addition, we improved the winner-take-all (WTA) mechanisms and R-STDP learning rule in the decoder based on the reward and punish decision, enabling the network to perform better. The experiments are performed on the N-MNIST, MNIST-DVS, and the DVS Gesture datasets, improving the accuracy of the best existing plasticity-based SNN by 0.19%, 0.27% and 1.35%, respectively. © 2016 IEEE.

关键词： Decoding

来源：评论

学校读者我要写书评

暂无评论

DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-Preserving Talking Face Synthesis

DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Kaijun Deng Dezhi Zheng Jindong Xie Jinbao Wang Weicheng Xie Linlin Shen Siyang Song Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Department of Computer Science University of Exeter

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Accurately synthesizing talking face videos and capturing fine facial features for individuals with long hair presents a significant challenge. To tackle these challenges in existing methods, we propose a decomposed per-embedding Gaussian fields (DEGSTalk), a 3D Gaussian Splatting (3DGS)-based talking face synthesis method for generating realistic talking faces with long hairs. Our DEGSTalk employs Deformable Pre-Embedding Gaussian Fields, which dynamically adjust pre-embedding Gaussian primitives using implicit expression coefficients. This enables precise capture of dynamic facial regions and subtle expressions. Additionally, we propose a Dynamic Hair-Preserving Portrait Rendering technique to enhance the realism of long hair motions in the synthesized videos. Results show that DEGSTalk achieves improved realism and synthesis quality compared to existing approaches, particularly in handling complex facial dynamics and hair preservation. Our code is available at https://***/CVI-SZU/DEGSTalk.

关键词： Hair Training Three-dimensional displays Dynamics Signal processing Rendering (computer graphics) Noise measurement Speech processing Faces Videos

来源：评论

学校读者我要写书评

暂无评论

Dual Encoders for Diffusion-based Image Inpainting

Dual Encoders for Diffusion-based Image Inpainting

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dezhi Zheng Kaijun Deng Jinbao Wang Linlin Shen Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Current diffusion-based inpainting models struggle to preserve unmasked regions or generate highly coherent content. Additionally, it is hard for them to generate meaningful content for 3D inpainting. To tackle these challenges, we design a plug-and-play branch that runs through the entire generation process to enhance existing models. Specifically, we utilize dual encoders - a Convolutional Neural Network (CNN) encoder and the pre-trained Variational AutoEncoder (VAE) encoder, to encode masked images. The latent code and the feature map from the dual encoders are fed to diffusion models simultaneously. In addition, we apply Zero-padded initialization to solve the problem of mode collapse caused by this branch. Experiments on BrushBench and EditBench demonstrate that models with our plug-and-play branch can improve the coherence of inpainting, and our model achieves new state-of-the-art results.

关键词： Visualization Three-dimensional displays Codes Autoencoders Coherence Signal processing Diffusion models Decoding Convolutional neural networks Speech processing

来源：评论

学校读者我要写书评

暂无评论

Equivalence Relation Analysis and Design of Repetitive Controllers and Multiple Quasi-Resonant Controllers for Single-Phase Inverters

引用

IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Zhao, Qiangsong Zhang, Hongwei Xia, Yuanqing Chen, Qiang Ye, Yongqiang Zhongyuan University of Technology School of Automation and Electrical Engineering Zhengzhou450007 China Beijing Institute of Technology School of Automation Beijing100081 China Zhejiang University of Technology Data-Driven Intelligent Systems Laboratory College of Information Engineering Hangzhou310023 China Nanjing University of Aeronautics and Astronautics College of Automation Engineering Nanjing211106 China

Repetitive controller (RC) and multiple quasi-resonant controllers (MQ-RSC), both based on internal model principle, are widely used in ac voltage/current control of single-phase inverters due to their high tracking accuracy for sinusoid reference and superior harmonics suppression capabilities. However, both controllers have their respective strengths and weaknesses regarding parameter design complexity, convergence rate, or stability margin. In this paper, the mathematical equivalence between various types of RCs and MQ-RSC are established comprehensively, which offers a new perspective for analyzing and designing these two types of controllers. The equivalent relations reveal the connection between the internal model coefficients of RCs and the equivalent resonant bandwidths of MQ-RSC. Based on this equivalent relations, a novel design way of RCs is proposed to enhance the frequency robustness. Furthermore, a new MQ-RSC design method is also put forward to simplify the design process. Experimental results on a 1.5-kW single-phase grid-tied inverter validate the accuracy of the equivalence relations and showcase their valuable roles in the design of RC and MQ-RSC. © 2013 IEEE.

关键词： Harmonic analysis

来源：评论

学校读者我要写书评

暂无评论

A Novel RC-ESO-ADRC for Harmonics Suppression and Robustness Improvement of Grid-Tied Inverters in a Weak and Distorted Grid

引用

IEEE Transactions on Power Electronics 2025年

作者： Zhao, Qiangsong Wang, Qifan Zhang, Hongwei Xia, Yuanqing Ye, Yongqiang Zhongyuan University of Technology School of Automation and Electrical Engineering Zhengzhou450007 China Zhejiang University of Technology Data-Driven Intelligent Systems Laboratory College of Information Engineering Hangzhou310023 China Beijing Institute of Technology School of Automation Beijing100081 China Nanjing University of Aeronautics and Astronautics College of Automation Engineering Nanjing211106 China

Repetitive control-based extented state observer (RC-ESO) enhances the estimation accuracy of periodic harmonic disturbances. However, it exhibits a slow convergence rate of disturbance estimation, limited steady-state estimation accuracy, and a lack of systematic design. An improved active disturbance rejection control (ADRC) current control scheme based on a novel RC-ESO (NRC-ESO) is proposed in this paper. An ideal compensation filter (ICF) is used to design the NRC-ESO, achieving a faster convergence rate for disturbance estimation. Additionally, a zero-phase low-pass filter is embedded in the NRC-ESO to reduce the current steady-state error and mitigate the impact of sensors high-frequency noise. The anti-disturbance ability, tracking performance, system stability, and detail parameter optimization design of the NRC-ESO-based ADRC (NRC-ESO-ADRC) are presented. The experimental results reflect the effectiveness of current harmonics suppression and robustness. © 1986-2012 IEEE.

关键词： Low pass filters

来源：评论

学校读者我要写书评

暂无评论

Feedforward Dual-Mode Repetitive Control for Single-Phase V2G Inverters in Residential Grids

引用

IEEE Transactions on Transportation Electrification 2025年

作者： Zhao, Qiangsong Zhou, Guohui Xia, Yuanqing Zhu, Yongsheng Yu, Dongdong Zhang, Hongwei Zhongyuan University of Technology School of Automation and Electrical Engineering Zhengzhou 450007 China Beijing Institute of Technology School of Automation Beijing 100081 China Zhejiang University of Technology Data-Driven Intelligent Systems Laboratory College of Information Engineering Hangzhou 310023 China

Vehicle-to-grid (V2G) technology enhances the utilization of renewable energy by enabling electric vehicles to inject stored renewable power into the grid. However, in singlephase residential grids, the grid background harmonics and grid frequency variations directly affect the feed-in grid current quality of the V2G inverter system. To address this issue, this paper proposes a novel feedforward dual-mode repetitive control (FDMRC) scheme, which adjusts adaptively the repetitive control (RC) gains according to the harmonics distribution of feed-in grid current. By introducing the feedforward channels into the dual-mode repetitive control, the FDMRC enlarges the open-loop gains at the frequencies of interest, while enhancing the dynamic performance of the system. In addition, the adaptive RC gains improve the harmonics suppression performance of the FDMRC system under varied grid background harmonics. Furthermore, stability analysis and detailed parameters optimization design for the FDMRC system are presented. The experimental platform of a single-phase V2G inverter is established to verify the effectiveness of the proposed FDMRC scheme, and the experimental results demonstrates that the proposed FDMRC scheme possesses enhanced harmonics suppression performance, strong frequency robustness, and fast dynamic performance under the scenarios of varied grid background harmonics and grid frequency variations. © 2015 IEEE.

关键词： dual-mode repetitive control grid background harmonics grid frequency variations harmonics suppression Vehicle-to-grid inverter

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：