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A rejection sampling algorithm for off-centered discrete Gaussian distributions over the integers

science China(information sciences) 2019年第3期62卷 196-198页

作者： Yusong DU Baodian WEI Huang ZHANG School of Data and Computer Science Sun Yat-sen University Guangdong Key Laboratory of Information Security Technology Chongqing Key Lab of Computer Network and Communication Technology

Dear editor,Discrete Gaussian sampling, that is, sampling from a discrete Gaussian distribution DΛ,σ,cwith parameter σ> 0 and center c∈R~n over an ndimensional lattice Λ, has been considered by the cryptography research community as one of the fundamental building blocks of lattice-based cryptography [1, 2]. The simplest lattice is the one-

关键词： A rejection sampling algorithm for off-centered discrete Gaussian distributions over the integers

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Image processing operations identification via convolutional neural network

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science China(information sciences) 2020年第3期63卷 275-281页

作者： Bolin CHEN Haodong LI Weiqi LUO Jiwu HUANG Guangdong Key Laboratory of Information Security Technology School of Data and Computer Science Sun Yat-sen University College of Information Engineering Shenzhen University

Dear editor,In recent years, image forensics has attracted increasing attention, and many forensic methods have been proposed for identifying image processing operations. Until now, most existing methods have been based on hand-crafted features, and only one specific operation is considered in these methods. In many forensic scenarios, however,

关键词： image Image processing operations identification via convolutional neural network

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Efficient Adaptive lab.l Refinement for lab.l Noise Learning

arXiv

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arXiv 2025年

作者： Zhang, Wenzhen Cheng, Debo Lu, Guangquan Zhou, Bo Li, Jiaye Zhang, Shichao School of Computer Science and Engineering Guangxi Normal University Guangxi Guilin541004 China Guangxi Key Lab of Multi-Source Information Mining & Security Guangxi Normal University Guangxi Guilin541004 China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guangxi Guilin541004 China UniSA STEM University of South Australia AdelaideSA5095 Australia Guangxi Collaborative Innovation Center of Modern Sericulture and Silk Hechi University Guangxi Hechi546300 China The State Key Laboratory of Blockchain and Data Security Zhejiang University Zhejiang Hangzhou310027 China

Deep neural networks are highly susceptible to overfitting noisy lab.ls, which leads to degraded performance. Existing methods address this issue by employing manually defined criteria, aiming to achieve optimal partitioning in each iteration to avoid fitting noisy lab.ls while thoroughly learning clean samples. However, this often results in overly complex and difficult-to-train models. To address this issue, we decouple the tasks of avoiding fitting incorrect lab.ls and thoroughly learning clean samples and propose a simple yet highly applicable method called Adaptive lab.l Refinement (ALR). First, inspired by lab.l refurbishment techniques, we update the original hard lab.ls to soft lab.ls using the model’s predictions to reduce the risk of fitting incorrect lab.ls. Then, by introducing the entropy loss, we gradually ‘harden’ the high-confidence soft lab.ls, guiding the model to better learn from clean samples. This approach is simple and efficient, requiring no prior knowledge of noise or auxiliary datasets, making it more accessible compared to Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning

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A Tutorial on Movable Antennas for Wireless Networks

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IEEE Communications Surveys and Tutorials 2025年

作者： Zhu, Lipeng Ma, Wenyan Mei, Weidong Zeng, Yong Wu, Qingqing Ning, Boyu Xiao, Zhenyu Shao, Xiaodan Zhang, Jun Zhang, Rui National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore National Key Laboratory of Wireless Communications Chengdu611731 China Southeast University National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai200240 China Beihang University School of Electronic and Information Engineering Beijing100191 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada Beijing Institute of Technology State Key Laboratory of CNS/ATM Miit Key Laboratory of Complex-field Intelligent Sensing Beijing100081 China The Chinese University of HongKong School of Science and Engineering Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China

Movable antenna (MA) has been recognized as a promising technology to enhance the performance of wireless communication and sensing by enabling antenna movement. Such a significant paradigm shift from conventional fixed antennas (FAs) to MAs offers tremendous new opportunities towards realizing more versatile, adaptive and efficient next-generation wireless networks such as 6G. In this paper, we provide a comprehensive tutorial on the fundamentals and advancements in the area of MA-empowered wireless networks. First, we overview the historical development and contemporary applications of MA technologies. Next, to characterize the continuous variation in wireless channels with respect to antenna position and/or orientation, we present new field-response channel models tailored for MAs, which are applicable to narrowband and wideband systems as well as far-field and near-field propagation conditions. Subsequently, we review the state-of-the-art architectures for implementing MAs and discuss their practical constraints. A general optimization framework is then formulated to fully exploit the spatial degrees of freedom (DoFs) in antenna movement for performance enhancement in wireless systems. In particular, we delve into two major design issues for MA systems. First, we address the intricate antenna movement optimization problem for various communication and/or sensing systems to maximize the performance gains achievable by MAs. Second, we deal with the challenging channel acquisition issue in MA systems for reconstructing the channel mapping between arbitrary antenna positions inside the transmitter and receiver regions. Moreover, we show existing prototypes developed for MA-aided communication/sensing and the experimental results based on them. Finally, the extension of MA design to other wireless systems and its synergy with other emerging wireless technologies are discussed. We also highlight promising research directions in this area to inspire future investigatio

关键词： Antenna arrays

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Generalized Block Markov Superposition Transmission over Free-Space Optical Links

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China Communications 2017年第9期14卷 80-93页

作者： Jinshun Zhu Shancheng Zhao Xiao Ma School of Electronics and Information Technology Sun Yat-sen University College of Information Science and Technology Jinan University School of Data and Computer Science Sun Yat-sen University Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University

In free-space optical(FSO) communications, the performance of the communication systems is severely degraded by atmospheric turbulence. Channel coding and diversity techniques are commonly used to combat channel fading induced by atmospheric turbulence. In this paper, we present the generalized block Markov superposition transmission(GBMST) of repetition codes to improve time diversity. In the GBMST scheme, information sub-blocks are transmitted in the block Markov superposition manner, with possibly different transmission memories. Based on analyzing an equivalent system, a lower bound on the bit-error-rate(BER) of the proposed scheme is presented. Simulation results demonstrate that, under a wide range of turbulence conditions, the proposed scheme improves diversity gain with only a slight reduction of transmission rate. In particular, with encoding memory sequence(0, 0, 8) and transmission rate 1/3, a diversity order of eleven is achieved under moderate turbulence conditions. Numerical results also show that, the GBMST systems with appropriate settings can approach the derived lower bound, implying that full diversity is achievable.

关键词： atmospheric turbulence channel block Markov superposition transmission(BMST) free-space optical communications gamma-gamma channel model sliding-window decoding algorithm time diversity

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Spatial-Modulated Physical-Layer Network Coding Based on Block Markov Superposition Transmission for Maritime Relay Communications

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China Communications 2020年第3期17卷 26-35页

作者： Yao Shi Liming Zheng Wenchao Lin Xiao Ma School of Data and Computer Science Sun Yat-sen UniversityGuangzhou 510006China Guangdong Key Laboratory of Information Security Technology Sun Yat-sen UniversityGuangzhou 510006China

As an alternative to satellite communications,multi-hop relay networks can be deployed for maritime long-distance *** from terrestrial environment,marine radio signals are affected by many factors,e.g.,weather conditions,evaporation ducting,and ship rocking caused by *** ensure the data transmission reliability,the block Markov superposition transmission(BMST)codes,which are easily configurable and have predictable performance,are applied in this ***,the physical-layer network coding(PNC)scheme with spatial modulation(SM)is adopted to improve the spectrum *** the BMST-SMPNC system,we propose an iterative algorithm,which utilizes the channel observations and the a priori information from BMST decoder,to compute the soft information corresponding to the XORed bits constructed by the relay *** results indicate that the proposed scheme outperforms the convolutional coded SM-PNC over fast-fading Rician ***,the performance can be easily improved in high spatial correlation maritime channel by increasing the memory m.

关键词： block Markov superposition transmission maritime relay communications physical-layer network coding Rician fading spatial modulation

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Calibration based universal JPEG steganalysis

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science in China(Series F) 2009年第2期52卷 260-268页

作者： HUANG FangJun1,2 & HUANG JiWu1,2 1 school of information science and technology,Sun Yat-Sen University,Guangzhou 510275,China 2 guangdong key lab. of information security technology,Sun Yat-Sen University,Guangzhou 510275,China School of Information Science and Technology Sun Yat-Sen University Guangzhou 510275 China Guangdong Key Lab. of Information Security Technology Sun Yat-Sen University Guangzhou 510275 China

For steganalysis of JPEG images, features derived in the embedding domain appear to achieve a preferable performance. However, with the existing JPEG steganography, the minor changes due to the hidden secret data are not easy to be explored directly from the quantized block DCT （BDCT） coefficients in that the energy of the carrier image is much larger than that of the hidden signal. In this paper, we present an improved calibration-based universal JPEG steganalysis, where the microscopic and macroscopic calibrations are combined to calibrate the local and global distribution of the quantized BDCT coefficients of the test image. All features in our method are generated from the difference signal be- tween the quantized BDCT coefficients of the test image and its corresponding microscopic calibrated image, or calculated as the difference between the signal extracted from test image and its corresponding macroscopic calibrated image. The extracted features will be more effective for our classification. Moreover, through using the Markov empirical transition matrices, both magnitude and sign dependencies along row scanning and column scanning patterns existed in intra-block and inter-block quantized BDCT coefficients are employed in our method. Experimental results demonstrate that our proposed scheme outperforms the best effective JPEG steganalyzers having been presented.

关键词： JPEG steganography steganalysis microscopic calibration macroscopic calibration

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Binary Image Steganalysis Based on Distortion Level Co-Occurrence Matrix

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computers, Materials & Continua 2018年第5期55卷 201-211页

作者： Junjia Chen Wei Lu Yuileong Yeung Yingjie Xue Xianjin Liu Cong Lin Yue Zhang School of Data and Computer Science Guangdong Key Laboratory of Information Security TechnologySun Yat-sen UniversityGuangzhou 510006China State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of SciencesBeijing 100093China Center for Faculty Development and Educational Technology Guangdong University of Finance and EconomicsGuangzhou 510320China Department of Computer Science College of Engineering and Computer ScienceEastern Lake AvenueOrlandoFLUSA.

In recent years,binary image steganography has developed so rapidly that the research of binary image steganalysis becomes more important for information *** most state-of-the-art binary image steganographic schemes,they always find out the flippable pixels to minimize the embedding *** this reason,the stego images generated by the previous schemes maintain visual quality and it is hard for steganalyzer to capture the embedding trace in spacial ***,the distortion maps can be calculated for cover and stego images and the difference between them is *** this paper,a novel binary image steganalytic scheme is proposed,which is based on distortion level co-occurrence *** proposed scheme first generates the corresponding distortion maps for cover and stego *** the co-occurrence matrix is constructed on the distortion level maps to represent the features of cover and stego ***,support vector machine,based on the gaussian kernel,is used to classify the *** with the prior steganalytic methods,experimental results demonstrate that the proposed scheme can effectively detect stego images.

关键词： Binary image steganalysis informational security embedding distortion distortion level map co-occurrence matrix support vector machine.

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Revealing the processing history of pitch-shifted voice using CNNs 10

Revealing the processing history of pitch-shifted voice usin...

引用

10th IEEE International Workshop on information Forensics and security, WIFS 2018

作者： Wang, Lihua Liang, Huixin Lin, Xiaodan Kang, Xiangui Guangdong Key Lab of Information Security Technology School of Data and Computer Science Sun Yat-sen University Guangzhou510006 China

ISBN: (纸本)9781538665367

With the emergence of varieties of audio editing software, disguised voices can be generated easily and are able to spoof the automatic speaker verification system. In this work, we focus on the spoofed voice disguised by pitch shifting which is commonly used by popular audio editing software and design a forensic chain to reveal the processing history of pitch-shifted voice signals blindly based on convolutional neural networks (CNNs). Because tracing the disguising tool before recognizing the disguising factor from the spoofed speech helps much to recognize the disguising factor, the proposed framework enables the detection of the disguised voice and the utilized tools. To our knowledge, it is the first attempt, focusing on voice transformation without any prior knowledge of the original speech, to recognize the disguising tool and the disguising factor from the spoofed speech, which is more suitable in real-world applications. Experimental results show that the disguising factors can be recognized with accuracy higher than 90%, indicating a high possibility of restoring the original voice from its disguised counterpart. © 2018 IEEE.

关键词： Convolution

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A Tutorial on Movable Antennas for Wireless Networks

arXiv

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arXiv 2025年

作者： Zhu, Lipeng Ma, Wenyan Mei, Weidong Zeng, Yong Wu, Qingqing Ning, Boyu Xiao, Zhenyu Shao, Xiaodan Zhang, Jun Zhang, Rui Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Chengdu611731 China National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China School of Electronic and Information Engineering Beihang University Beijing100191 China Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada State Key Laboratory of CNS/ATM MIIT Key Laboratory of Complex-field Intelligent Sensing Beijing Institute of Technology Beijing100081 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

关键词： Antenna arrays

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