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A Stronger Leakage Model Based on Conditional Generative Adversarial Networks for Correlation Power Analysis

International Journal of Circuit Theory and Applications 2025年第0期

作者： Tang, Cheng Li, Lang Ou, Yu College of Computer Science and Technology Hengyang Normal University Hengyang China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal University Hengyang China

Deep learning-based side-channel attacks (DL-SCA) have attracted widespread attention in recent years, and most of the researchers are devoted to finding the optimal DL-SCA method. At the same time, traditional SCA methods have lost their luster. However, traditional attacks still have certain advantages. Compared with the DL-SCA method, they do not require cumbersome engineering of tuning DL models and hyperparameters, making them easier to implement. Correlation power analysis (CPA), as a traditional SCA method, is still widely used in various analysis scenarios and plays an important role. In CPA, the leakage model is the key to simulating the power consumption, and it decides the attack efficiency. However, the existing leakage models are designed based on theory but ignore the actual attack scene. We found that conditional generative adversarial networks (CGAN) can ideally learn the target device's leakage characteristics and real power consumption. We let CGAN pre-learn the leakage of the target device, and then make the generator as the leakage model (Formula presented.). The (Formula presented.) leakage model can characterize the leakages of the device and consider the presence of noise in the actual scenario. It can map the power consumption more realistically and accurately, which can lead to a more powerful CPA attack. In this work, three kinds of (Formula presented.) leakage models ((Formula presented.) 1, (Formula presented.) 2, and (Formula presented.) 3 leakage models) corresponding to the labels least significant bit (LSB), hamming weight (HW), and identity (ID) of CGAN are discussed. The experimental results show that the (Formula presented.) 3 leakage model has better attack performance. Compared with the ordinary HW leakage model, the number of traces needed to recover the key on the ASCAD and SAKURA-AES datasets reduced by about 38.9% and 85.9%, respectively. © 2025 John Wiley & Sons Ltd.

关键词： Generative adversarial networks

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Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Tian, Zhuojun Zhang, Zhaoyang Li, Yiwei Bennis, Mehdi Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China University of Oulu Center for Wireless Communications Oulu90014 Finland State Key Laboratory of Industrial Control Technology Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multimodal Communication Networks and Intelligent Information Processing Hangzhou310027 China Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen361024 China

To jointly tackle the challenges of data and node heterogeneity in decentralized learning, we propose a distributed strong lottery ticket hypothesis (DSLTH), based on which a communication-efficient personalized learning algorithm is developed. In the proposed method, each local model is represented as the ‌Hadamard product of global real-valued parameters and a personalized binary mask for pruning. The local model is learned by updating and fusing the personalized binary masks while the real-valued parameters are fixed among different agents. To further reduce the complexity of hardware implementation, we incorporate a group sparse regularization term in the loss function, enabling the learned local model to achieve structured sparsity. Then, a binary mask aggregation algorithm is designed by introducing an intermediate aggregation tensor and adding a personalized fine-tuning step in each iteration, which constrains model updates towards the local data distribution. The proposed method effectively leverages the relativity among agents while meeting personalized requirements in heterogeneous node conditions. We also provide a theoretical proof for the DSLTH, establishing it as the foundation of the proposed method. Numerical simulations confirm the validity of the DSLTH and demonstrate the effectiveness of the proposed algorithm. © 2015 IEEE.

关键词： Iterative methods

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MMR-HAD: Multi-scale Mamba Reconstruction Network for Hyperspectral Anomaly Detection

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IEEE Transactions on Geoscience and Remote Sensing 2025年

作者： Fu, Xiyou Zhang, Ting Cheng, Juan Jia, Sen Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Shaanxi Key Laboratory of Optical Remote Sensing and Intelligent Information Processing Xi'an710119 China

Hyperspectral anomaly detection (HAD) aims to identify anomalous objects from hyperspectral images (HSIs) whose spectral features significantly deviate from their surroundings. Existing HAD methods still reconstruct some anomalies during the background reconstruction process, which can seriously affect the detection accuracy. Consequently, inspired by Mamba's ability to effectively model long sequences, we propose a Multi-scale Mamba Reconstruction Network for Hyperspectral Anomaly Detection (MMR-HAD) by enhancing the representation of the background and inhibit anomalies from being reconstructed. MMR-HAD first removes most of the anomalous pixels in hyperspectral images using the Random Mask (RM) strategy, which reduces the interference of anomalous pixels on the background reconstruction and makes the background features more prominent. To further filter out the small amount of residual anomalous pixels, we propose the Multi-scale Dilated Attention Background Enhancement (MDABE) mechanism, which enhances the background representation. Finally, we apply the Multi-scale Dynamic Feature Fusion (MDFF) strategy to reconstruct the background image, further extracting and strengthening the background information, thus obtaining a pure background image. MMR-HAD, which focuses on generating a pure background, has been experimentally validated on seven real hyperspectral datasets. The results demonstrate that it excels in background enhancement and anomaly suppression, significantly improving detection accuracy. Introducing Mamba offers a promising solution for HAD, with substantial potential for practical application. © 1980-2012 IEEE.

关键词： Image reconstruction

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Large-Scale Riemannian Meta-Optimization via Subspace Adaptation

arXiv

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arXiv 2025年

作者： Yu, Peilin Wu, Yuwei Gao, Zhi Fan, Xiaomeng Jia, Yunde Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China

Riemannian meta-optimization provides a promising approach to solving non-linear constrained optimization problems, which trains neural networks as optimizers to perform optimization on Riemannian manifolds. However, existing Riemannian meta-optimization methods take up huge memory footprints in large-scale optimization settings, as the learned optimizer can only adapt gradients of a fixed size and thus cannot be shared across different Riemannian parameters. In this paper, we propose an efficient Riemannian meta-optimization method that significantly reduces the memory burden for large-scale optimization via a subspace adaptation scheme. Our method trains neural networks to individually adapt the row and column subspaces of Riemannian gradients, instead of directly adapting the full gradient matrices in existing Riemannian meta-optimization methods. In this case, our learned optimizer can be shared across Riemannian parameters with different sizes. Our method reduces the model memory consumption by six orders of magnitude when optimizing an orthogonal mainstream deep neural network (e.g. ResNet50). Experiments on multiple Riemannian tasks show that our method can not only reduce the memory consumption but also improve the performance of Riemannian meta-optimization. © 2025, CC BY-NC-ND.

关键词： Constrained optimization

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MFA-LJP: IMPROVED LEGAL JUDGMENT PREDICTION USING BERT EMBEDDINGS-BASED MULTIFRACTAL ANALYSIS

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Fractals 2025年

作者： Guan, Jian-Hua Yu, Zu-Guo Sun, Xin-Gen National Center for Applied Mathematics in Hunan Xiangtan University Hunan 411105 China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan 411105 China

Legal Judgment Prediction (LJP) is the task of automatically predicting judgment results based on case fact descriptions. However, these fact descriptions can be regarded as lengthy and complex chains of events, and traditional text feature extraction methods struggle to capture the complex multi-scale features from them. To address this, we propose a legal judgment prediction model based on multifractal analysis and BERT embeddings (MFA-LJP). In this work, we treat the [CLS] embeddings from BERT as one-dimensional time series and perform multifractal analysis to reveal the multifractal properties that these time series exhibit. Furthermore, for each time series, we extract multifractal parameters and concatenate them with the corresponding output from the BiGRU to create a new feature vector. Finally, this feature vector is used as the input to a fully connected layer for the prediction tasks of applicable law articles, charges, and terms of penalty, respectively. Experiments on CAIL datasets demonstrate the effectiveness of MFA. With only 11 multifractal parameters added, MFA-LJP can achieve considerable performance improvement in three sub-tasks. Compared to the current best baseline model HD-LJP, on the CAIL-small dataset, MFA-LJP improves the F1 scores for applicable law articles, charges, and terms of penalty by 5.63%, 2.54%, and 2.95%, respectively. Similarly, on the CAIL-big dataset, MFA-LJP also shows notable achievements, with F1 scores for applicable law articles, charges, and terms of penalty improving by 3.08%, 4.33%, and 8.91%, respectively. © 2025 World Scientific Publishing Company.

关键词： BERT Embedding Legal Judgment Prediction Multifractal Analysis

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WiViPose: A Video-aided Wi-Fi Framework for Environment-Independent 3D Human Pose Estimation

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IEEE Transactions on Multimedia 2025年

作者： Zhang, Lei Ning, Haoran Tang, Jiaxin Chen, Zhenxiang Zhong, Yaping Han, Yahong Tianjin University College of Intelligence and Computing the Tianjin Key Laboratory of Advanced Network Technology and Application Tianjin300050 China Key Laboratory of Computing Power Network and Information Security Ministry of Education China University of Jinan Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing the School of Information Science and Engineering Jinan250022 China Wuhan Sports University Sports Big-data Research Center Wuhan430079 China Tianjin University College of Intelligence and Computing the Tianjin Key Laboratory of Machine Learning Tianjin300350 China

The inherent complexity of Wi-Fi signals makes video-aided Wi-Fi 3D pose estimation difficult. The challenges include the limited generalizability of the task across diverse environments, its significant signal heterogeneity, and its inadequate ability to analyze local and geometric information. To overcome these challenges, we introduce WiViPose, a video-aided Wi-Fi framework for 3D pose estimation, which attains enhanced cross-environment generalization through cross-layer optimization. Bilinear temporal-spectral fusion (BTSF) is initially used to fuse the time-domain and frequency-domain features derived from Wi-Fi. Video features are derived from a multiresolution convolutional pose machine and enhanced by local self-attention. Cross-modality data fusion is facilitated through an attention-based transformer, with the process further refined under a supervisory mechanism. WiViPose demonstrates effectiveness by achieving an average percentage of correct keypoints (PCK)@50 of 91.01% across three typical indoor environments. © 2025 IEEE.

关键词： Frequency domain analysis

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HTL-WASI preconditioner for finite element discretization of complex ADR equation and its application

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Journal of Computational and Applied Mathematics 2025年 470卷

作者： Yang, Ronghua Wang, Junxian Shu, Shi School of Mathematics and Computational Science Xiangtan University Xiangtan Hunan411105 China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education National Center for Applied Mathematics in Hunan Xiangtan Hunan411105 China Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Xiangtan Hunan411105 China

The convection diffusion reaction (ADR) equation is widely used in engineering applications. In this paper, we focus on a complex ADR equation arising from Helmholtz equation with impedance boundary conditions, prove the coerceiveness and boundedness of the sesquilinear functional and discuss the fast solver of the discretization. Firstly, we design a Weighted Additive Schwarz with Impedance (WASI) preconditioner, in order to overcome the dependence of the WASI preconditioner on the number of subdomains and the overlapping width, an ideal coarse space is designed. Although it can be proved that the corresponding hybrid two-level preconditioner is the exact inverse of the ADR coefficient matrix, the dimension of this coarse space is too big. In order to overcome this deficiency, a smaller dimensional coarse space was constructed by introducing local generalized eigenvalue problems (GEP) on each subdomain, and the descent rate of the corresponding two-level preconditioned GMRES method was rigorously analyzed which does not depend on mesh size, overlapping width, wave number k, and the absorption parameter. Since the size of the coarse space is sensitive to k and the GEP on each subdomain needs to be solved, the computational cost is too high. Therefore, we design an iterative method based on an economical two-level preconditioner, and establish a heuristic convergence theory that is supported by numerical results. Numerical experiments show the robustness of GMRES with the corresponding economical two-level preconditioner(HTLE-WASI-GMRES). Finally, for the Helmholtz finite element discretization, by using the Shifted Laplace technique and ADR equation, we design a novel fast solver combining with HTLE-WASI-GMRES for ADR equation. Numerical experiments have demonstrated the effectiveness of the algorithm. © 2025

关键词： Convergence of numerical methods

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Research and application of Gaussian coupled control bistable stochastic resonance system with independent parameter regulation

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Results in Physics 2025年 73卷

作者： Hou, Tiantian Jiao, Shangbin Wang, Yi Song, Nianlong Li, Jianghua Cui, Wenchuan Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an 710048 China

The theory of Stochastic Resonance (SR) has drawn significant attention due to its exceptional ability to detect faint signals. Despite this, research to date indicates that for SR systems, whether they are monostable, bistable, or multi-stable, modifications to the system parameters lead to concurrent alterations in the depth and breadth of the potential wells when analyzing engineering signals, which results in suboptimal detection outcomes. To address these issues, a two-dimensional Gaussian bistable coupled SR (GBCSR) system has been proposed that can individually adjust the potential well characteristics. This innovative system facilitates the separate adjustment of shape characteristics of potential, allowing for more precise manipulation of the system's dynamic response. The system's non-linear dynamic traits are explicated through an analysis of the steady-state probability density (SPD) function and the mean first passage time (MFPT), substantiating the effectiveness of the new model. In practical scenarios, a variety of bearing defect signals serve as the objects of detection. The structural parameters of the GBCSR system are co-optimized using the Brain Storm Optimization (BSO) algorithm. This optimization approach leverages the algorithm's ability to enhance population diversity and improve convergence accuracy, thereby optimizing the system's performance. The experimental outcome results show that the proposed system can accurately detect the frequency of bearing fault signals. When compared with traditional SR systems such as the traditional bistable stochastic SR (TBSR), the traditional Gaussian SR system (TGSR), and the cascade stochastic resonance system, the proposed coupled system demonstrates superior performance. This is achieved through the transfer of energy or information between subsystems, which enables more efficient utilization of noise energy. The system can trigger the resonance effect over a broader range of noise intensities and signi

关键词： Bearing fault detection Coupled stochastic resonance Independent parameter regulation MFPT SPD

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A tighter bound on the minimum distances for an infinite family of binary BCH codes and its generalization

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Finite Fields and their Applications 2025年 108卷

作者： Lu, Haodong Wang, Xuan Shi, Minjia The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Anhui Hefei230601 China

In this paper, we improve the bound on the minimum distance for the family of binary cyclic codes proposed by Sun et al. (2024) [8]. The 3-ary analogue is also studied in this paper, which is a nice family of ternary cyclic codes that contains some best known linear codes, and this family has a better lower bound on minimum distance than that of codes proposed by Chen et al. (2023) [2]. © 2025 Elsevier Inc.

关键词： BCH code Cyclic code Linear code

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Movable Antenna Enhanced Downlink Multi-User Integrated Sensing and Communication System

arXiv

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arXiv 2025年

作者： Han, Yanze Li, Min Zhao, Xingyu Zhao, Ming-Min Zhao, Min-Jian College of Information Science and Electronic Engineering China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Zhejiang University Hangzhou310027 China

This work investigates the potential of exploiting movable antennas (MAs) to enhance the performance of a multiuser downlink integrated sensing and communication (ISAC) system. Specifically, we formulate an optimization problem to maximize the transmit beampattern gain for sensing while simultaneously meeting each user’s communication requirement by jointly optimizing antenna positions and beamforming design. The problem formulated is highly non-convex and involves multivariate-coupled constraints. To address these challenges, we introduce a series of auxiliary random variables and transform the original problem into an augmented Lagrangian problem. A double-loop algorithm based on a penalty dual decomposition framework is then developed to solve the problem. Numerical results validate the effectiveness of the proposed design, demonstrating its superiority over MA designs based on successive convex approximation optimization and other baseline approaches in ISAC systems. The results also highlight the advantages of MAs in achieving better sensing performance and improved beam control, especially for sparse arrays with large apertures. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

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