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Chinese Journal of Electronics 2025年第3期23卷 454-457页

作者： Hongxi Tong Wenhai Chen Fei Yu Department of Mathematics Shanghai University Shanghai China Science and Technology on Communication Security Laboratory Chengdu China

Linear codes with Singleton defects equivalent to 0, 1, 2 are studied, and they have good properties. They are called MDS codes, NMDS codes, NNMDS cdoes, respectively. In this paper, we study linear codes with large Singleton defects and these codes have same good properties with MDS codes, NMDS codes, NNMDS codes. We call them $\boldsymbol{l}$ -th NMDS codes. A series of iff conditions of $\boldsymbol{l}$ -th NMDS codes is presented. And we give an upper bound on length of $\boldsymbol{l}$ -th NMDS codes. In the last, some examples of $\boldsymbol{l}$ -th NMDS codes are given.

关键词： Codes Upper bound Linear codes Hamming weight

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DETCP: Self-Detoxifying Language Models With Contrastive Pairs

DETCP: Self-Detoxifying Language Models With Contrastive Pai...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Dianqing Liu, Yi Guo, Junbo Mao, Zhendong School of Cyberspace Security University of Science and Technology of China Hefei China State Key Laboratory of Communication Content Cognition People's Daily Online Beijing China

ISBN: (纸本)9798350368741

Influenced by context such as tone, emotion and demographic, pre-trained language models may generate harmful text, which limits their widespread application. While detoxifying language models seeks to reduce the likelihood of generating harmful content. There are two categories of detoxification strategies: fine-tuning language models and constraining outputs during inference. Neither category of methods achieved a proper balance between detoxification efficacy, the amount of annotated data, and inference efficiency. In this paper, we introduce a lightweight detoxification approach aiming at guiding the probability distribution of generated tokens towards the opposite direction of toxification, which relies on the language model itself and the contrastive pairs of contexts in the inference phase, without training. Experiments show that our method has state-of-the-art performance in detoxification effect while it has an edge in both fluency and speed of text generation. © 2025 IEEE.

关键词： controlled text generation detoxifying language models pretrained language model prompt learning

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Multi-scale network for single image deblurring based on ensemble learning module

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Multimedia Tools and Applications 2025年第11期84卷 9045-9064页

作者： Wu, WenBo Pan, Yun Su, Na Wang, JingTao Wu, ShaoChuan Xu, ZeKun Yu, YouJian Liu, YaPeng State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China School of Computer and Cyberspace Security Communication University of China Beijing100024 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China College of Computer and Control Engineering Northeast Forestry University Harbin150040 China School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China School of Computer Science and Technology Anhui University Hefei230601 China

In this paper, we have identified two primary issues with current multi-scale image deblurring methods. On the one hand, the blurring scale is ignored. On the other hand, the context information of images is not fully utilized, and the spatial detail information of images is lost. To better solve the above problems, on top of that, this paper proposes a multi-scale network for single image deblurring. The designed network contains two parts: 1) Ensemble Learning Model: The Weak Learning Model in this module connects multiple weak learners in series to increase network depth. Moreover, through multi-scale feature ensemble module, the output features and intermediate features of different weak learner are integrated, and finally strong learner is used to enhance the features with semantic information at different scales;2) Spatial Feature Enhancement Module: This module can preserve spatial detail information of images and compensate for the disadvantages of losing image spatial details due to continuous downsampling in the Ensemble Learning Model structure. Our proposed deblurring model has been extensively evaluated on several benchmark datasets, and the model achieves superior performance compared to the most advanced deblurring methods. In particular, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) values are 33.57 dB and 0.965 on the GoPro dataset, respectively, with PSNR and SSIM improving by 0.49 dB and 0.002 over NAFNet, and 0.65 dB and 0.004 over Restormer. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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Radio Signal Modulation Pattern Recognition Based on Time-Frequency Adaptive Decomposition and Hybrid Neural Network

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IEEE Internet of Things Journal 2025年

作者： Dai, Shaogang Xu, Jianwei Zhou, Huaji Hangzhou Dianzi University School of communication engineering Hangzhou310018 China Science and Technology on Communication Information Security Control Laboratory Jiaxing314000 China

Automatic modulation classification of electromagnetic signals has an important role in the field of signal processing. The current methods for identifying the modulation pattern of radio signals mainly used end-to-end neural network models, which suffered from the problems such as network redundancy and non-interpretability. In order to solve the above problems, this paper proposes a radio signal modulation pattern recognition method based on time-frequency adaptive decomposition and hybrid neural network. The discrete wavelet decomposition is used to decompose the original radio signals into adaptive high and low frequencies. Then feed the resulting high and low frequency portions into a multi-channel hybrid neural network (MHNN) for training to obtain the classification results. In the discrete wavelet decomposition process, the number of decomposition layers and the decomposition threshold are determined adaptively according to the difference of SNRs. In the design of multi-path hybrid neural network structure, the signal characteristics of high and low frequency parts are considered comprehensively. The network channels with different convolutional kernel scales and different layers are constructed to reduce the number of model parameters and training time while ensuring the classification accuracy. Using RadioML2016.10a dataset for experiments, the recognition accuracy of MHNN is prior to the end-to-end deep learning methods, which can achieve the accuracy of 92.30% and the F1 of 91.84% when SNR=2dB. It shows that the proposed radio signal modulation pattern recognition method based on the time-frequency adaptive decomposition and hybrid neural network can balance the accuracy and lightweight. Moreover, it has certain interpretability for the original input data, distinguishing the main body and details of the signal by dividing the signal into high and low frequencies, which provides a new idea for the interpretability of deep convolutional neural networks. ©

关键词： Signal modulation

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Vertin: Fast, communication-friendly and Key-compact secure inference system for NNs and LLMs

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Journal of Information security and Applications 2025年 91卷

作者： Bie, Xin Liu, Zhenhua Liang, Han School of Mathematics and Statistics Xidian University Xi'an710071 China Science and Technology on Communication Security Laboratory Chengdu610041 China

Existing secure inference schemes based on function secret sharing (FSS) allow the client to obtain inference results while protecting the client's inputs, the server's neural networks (NNs), and large language models (LLMs), ensuring high online efficiency. However, there is still room for improvement in terms of storage, communication, and inference speed for linear layers in these schemes. In this work, we introduce a novel semi-honest secure two-party inference system tailored for NNs and LLMs, which surpasses state-of-the-art solutions in speed, communication efficiency, and key storage. Our system leverages plaintext weight matrices for the server, introducing FMLO, a secure two-party computation protocol supporting linear operations. By using precomputed random matrices correlated with weight matrices, FMLO minimizes key storage, online computation, and communication demands. We also develop two efficient protocols, πMulPre for matrix multiplication and πConvPre for matrix convolution, by using vector oblivious linear evaluation. Both protocols batch-generate required random numbers securely in the offline phase, reducing preprocessing overhead in FMLO. Compared to the leading FSS-based scheme Orca, Vertin reduces key storage by 5.37%, online communication by 16.46%, and online inference time by 10.71% in secure inference with ResNet-50. When compared to the state-of-the-art SIGMA on BERT-large model with the sequence length of 64, Vertin achieves reductions in key storage, online communication, and online runtime by 9.81%, 9.17%, and 8.9% respectively. © 2025

关键词： Matrix algebra

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Delayed interference-based broadband optical chaos generation in a mutual coupled RSOA and DFB system

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Optics Letters 2025年第10期50卷 3381-3384页

作者： Jiahao Qian Ning Jiang Anran Li Yiqun Zhang Bingjie Xu Wei Huang Kun Qiu School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu 611731 China Tianfu Jiangxi Laboratory Chengdu 641419 China National Key Laboratory of Security Communication Chengdu 610041 China

In this Letter, a delayed interference (DI)-based chaos generation scheme in a mutually coupled system consisting of a reflective semiconductor optical amplifier (RSOA) and a distributed feedback (DFB) semiconductor laser is proposed. We experimentally demonstrate its capability of generating broadband chaos with a large effective bandwidth and a flat power spectrum, and the influences of relevant parameters on the system performance are thoroughly analyzed. Compared to the conventional optical feedback-based chaos generation schemes, the proposed system can simultaneously output two chaotic signals with enhanced bandwidth and high spectral flatness. Moreover, the time-delay signature (TDS) of chaotic signals generated by this configuration is also effectively suppressed.

关键词： Distributed feedback lasers Passive optical networks Random number generation Semiconductor lasers Semiconductor optical amplifiers Variable optical attenuators

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Toward Universal Belief Propagation Decoding for Short Binary Block Codes

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IEEE Journal on Selected Areas in communications 2025年第4期43卷 1135-1152页

作者： Shen, Yifei Li, Zongyao Ren, Yuqing Boutillon, Emmanuel Balatsoukas-Stimming, Alexios Zhang, Chuan You, Xiaohu Burg, Andreas Telecommunications Circuits Laboratory Lausanne1015 Switzerland Southeast University LEADS National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Nanjing211189 China Southeast University Purple Mountain Laboratories Nanjing211189 China Université Bretagne Sud Lab-STICC CNRS UMR 6285 Lorient56100 France Eindhoven University of Technology Department of Electrical Engineering Eindhoven5600 MB Netherlands

Belief propagation (BP) decoding has been recognized for its capacity-approaching performance and high throughput when decoding long low-density parity-check (LDPC) codes. However, the application of BP decoding for short codes is hindered by dense parity-check matrices (PCMs) and prevalent short cycles in the Tanner graph. In this paper, we introduce a general method to extract an optimized sparse PCM for short binary block codes, which removes length-four cycles and enhances the connectivity of short cycles to enable BP decoding with improved performance. Notably, for short binary codes with lengths up to 64, our BP decoding performance approaches the maximum likelihood bound and surpasses the best-reported BP results with reduced computational complexity. Compared with other universal decoding algorithms, BP decoding using our extracted sparse PCMs is competitive in terms of both error-rate performance and computational complexity. These promising results suggest that our method to improve BP decoding for short codes is a step toward a practical universal BP decoder for next-generation communication systems. © 1983-2012 IEEE.

关键词： Block codes

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Performance Limits of Shadowing Effects-Based Passive Target Localization Assisted by Active Sensor Calibration via Visible Light Positioning

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IEEE Transactions on communications 2025年

作者： Bao, Xu Zhou, Jiawei Zhang, Licheng The School of Computer Science and Communication Engineering Jiangsu University Zhenjiang212013 China The Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace Zhenjiang212013 China

In this paper, we aim at exploring the performance limits of passive indoor target localization utilizing multiple light-emitting diode (LED) transmitters embedded into the ceiling and multiple photodiode (PD) sensors anchored on the floor, where the precise locations of the PDs are not predetermined. This work enriches our understanding of error evolution in the time-domain localization cooperation. We propose a novel solution to estimate the target position by identifying the PDs occluded by the target, through measuring the changes in received signal strength (RSS) caused by the shadowing effects induced by this indoor target. Concurrently, these LEDs are used for the positional calibration of the PDs involved in the localization process. Specifically, we derive the Cramér-Rao lower bound (CRLB) on the estimation errors for both target localization and sensor position calibration, and then conduct the convergence and asymptotic performance analyses based on the obtained error bounds. Finally, simulation results corroborate the performance analysis, elucidating how the localization error evolves until it converges, and identifying the factors that influence this intrinsic convergence mechanism. The derived CRLBs and the long-term localization performance from these bounds provide a theoretical basis for optimizing passive positioning algorithms without requiring any auxiliary receiver. © 1972-2012 IEEE. All rights reserved.

关键词： Cramer-Rao bounds

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PB-UAP: Hybride Universal Adversarial Attack for Image Segmentation

PB-UAP: Hybride Universal Adversarial Attack for Image Segme...

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

作者： Yufei Song Ziqi Zhou Minghui Li Xianlong Wang Hangtao Zhang Menghao Deng Wei Wan Shengshan Hu Leo Yu Zhang Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology School of Computer Science and Technology Huazhong University of Science and Technology School of Software Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

With the rapid advancement of deep learning, the model robustness has become a significant research hotspot, i.e., adversarial attacks on deep neural networks. Existing works primarily focus on image classification tasks, aiming to alter the model’s predicted labels. Due to the output complexity and deeper network architectures, research on adversarial examples for segmentation models is still limited, particularly for universal adversarial perturbations. In this paper, we propose a novel universal adversarial attack method designed for segmentation models, which includes dual feature separation and low-frequency scattering modules. The two modules guide the training of adversarial examples in the pixel and frequency space, respectively. Experiments demonstrate that our method achieves high attack success rates surpassing the state-of-the-art methods, and exhibits strong transferability across different models.

关键词： Training Image segmentation Perturbation methods Semantics Scattering Signal processing Predictive models Network architecture Robustness Speech processing

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OS-SEI: Open-Set Specific Emitter Identification Based on Outlier Exposure and Label Smoothing

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IEEE Internet of Things Journal 2025年

作者： Wang, Juzhen Wang, Hanhong Wang, Wei Zha, Haoran Hangzhou Dianzi University School of Communication Engineering Hangzhou310018 China Binjiang Institute of Artificial Intelligence of Zhejiang University of Technology Hangzhou310052 China Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China Harbin Engineering University College of Information and Communication Engineering Harbin150009 China

Specific Emitter Identification (SEI), based on the inevitable hardware imperfections of emitters, plays a crucial role in physical layer security, independent of upper-layer cryptographic methods. In recent years, SEI combined with deep learning has demonstrated exceptional performance and considerable potential. However, most SEI methods are designed for closed, independent identically distributed environments, which contrasts with real-world open scenarios. This discrepancy leads to significant security vulnerabilities when deploying models in practical applications. To address this challenge, we propose a novel Open-Set SEI (OS-SEI) framework, Outlier Exposure with Label Smoothing (OELS), which utilizes partial exposure to auxiliary anomalous data during the training phase and introduces specific loss functions to mitigate the open-set risk. By constructing smooth decision boundaries through label smoothing and applying thresholds to class confidence, our SEI framework demonstrates robust open-set authentication capabilities. Extensive experiments conducted on ADS-B and WiFi datasets show that the OELS method achieves outstanding open-set recognition performance, with F1 scores of 83.77% and 93.14% at an openness level of 0.10557. Furthermore, ablation experiments confirm the effectiveness and potential of our framework’s components. By increasing the diversity of auxiliary unknown data, we further improve performance in OS-SEI tasks. © 2014 IEEE.

关键词： Hardware security

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