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International Conference on Wireless communications and Signal Processing (WCSP)

作者： Zhigang Song Jian Sun Wensheng Zhang Cheng-Xiang Wang Minggao Zhang Shandong Provincial Key Lab of Wireless Communication Technologies School of Information Science and Engineering Shandong University Qingdao China The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Nanjing China National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories Nanjing China

ISBN: (数字)9781728172361

ISBN: (纸本)9781728172378

In this paper, a two-stage channel estimation scheme is presented for frequency-selective millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems with hybrid precoding. Specifically, at the first stage, likelihood function with respective to the channel angle of departure (AoD) and angle of arrival (AoA) are maximized by the deterministic maximum likelihood (DML) estimator to obtain a coarse channel estimate. At the second stage, a space-alternating generalized expectation-maximization (SAGE) based algorithm is proposed to refine the channel parameters. In addition, an angle paring scheme is proposed to paired the AoA and AoD. Simulation results show the better performance of the proposed method.

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NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results

NTIRE 2023 Challenge on Light Field Image Super-Resolution: ...

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Wang, Yingqian Wang, Longguang Liang, Zhengyu Yang, Jungang Timofte, Radu Guo, Yulan Jin, Kai Wei, Zeqiang Yang, Angulia Guo, Sha Gao, Mingzhi Zhou, Xiuzhuang Van Duong, Vinh Huu, Thuc Nguyen Yim, Jonghoon Jeon, Byeungwoo Liu, Yutong Cheng, Zhen Xiao, Zeyu Xu, Ruikang Xiong, Zhiwei Liu, Gaosheng Jin, Manchang Yue, Huanjing Yang, Jingyu Gao, Chen Zhang, Shuo Chang, Song Lin, Youfang Chao, Wentao Wang, Xuechun Wang, Guanghui Duan, Fuqing Xia, Wang Wang, Yan Xia, Peiqi Wang, Shunzhou Lu, Yao Cong, Ruixuan Sheng, Hao Yang, Da Chen, Rongshan Wang, Sizhe Cui, Zhenglong Chen, Yilei Lu, Yongjie Cai, Dongjun An, Ping Salem, Ahmed Ibrahem, Hatem Yagoub, Bilel Kang, Hyun-Soo Zeng, Zekai Wu, Heng National University of Defense Technology China Aviation University of Air Force China University of Würzburg Germany Eth Zürich Switzerland Sun Yat-sen University The Shenzhen Campus of Sun Yat-sen University China Bigo Technology Pte. Ltd. Singapore Smart Medical Innovation Lab Beijing University of Posts and Telecommunications China Global Explorer Ltd. Suzhou China National Engineering Research Center of Visual Technology School of Computer Science Peking University China School of Artificial Intelligence Beijing University of Posts and Telecommunications China Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of University of Science and Technology of China China School of Electrical and Information Engineering Tianjin University China Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University China Beijing Normal University China Toronto Metropolitan University Canada Beijing Institute of Technology China Shenzhen MSU-BIT University China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beihang Hangzhou Innovation Institute Yuhang China Faculty of Applied Sciences Macao Polytechnic University China School of Communication and Information Engineering Shanghai University China School of Information and Communication Engineering Chungbuk National University Korea Republic of Guangdong University of Technology China

ISBN: (纸本)9798350302493

In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4. This challenge develops a new LF dataset called NTIRE-2023 for validation and test, and provides a toolbox called BasicLFSR to facilitate model development. Compared with single image SR, the major challenge of LF image SR lies in how to exploit complementary angular information from plenty of views with varying disparities. In total, 148 participants have registered the challenge, and 11 teams have successfully submitted results with PSNR scores higher than the baseline method LF-InterNet [1]. These newly developed methods have set new state-of-the-art in LF image SR, e.g., the winning method achieves around 1 dB PSNR improvement over the existing state-of-the-art method DistgSSR [2]. We report the solutions proposed by the participants, and summarize their common trends and useful tricks. We hope this challenge can stimulate future research and inspire new ideas in LF image SR. © 2023 IEEE.

关键词： Image enhancement

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Characterizing Kerr Optical Frequency Combs Using Quantum Interference

Characterizing Kerr Optical Frequency Combs Using Quantum In...

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Asia communications and Photonics Conference and Exhibition (ACP)

作者： Jin Guo Yun-Ru Fan Yong Geng Guang-Wei Deng Hai-Zhi Song You Wang Li-Xing You Zhen Wang Heng Zhou Kun Qiu Guang-Can Guo Qiang Zhou Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu China Key Lab of Optical Fiber Sensing and Communication Networks University of Electronic Science and Technology of China Chengdu China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Southwest Institute of Technical Physics Chengdu China Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei China

For the first time, we characterize the properties of Kerr optical frequency combs using quantum optics. The temporal FWHM of single soliton state is obtained as $\boldsymbol{109.8\pm 6.5}$ fs within the Fourier tra...

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Measuring the Orbital-Angular-Momentum Complex Spectrum of Light with the Fast Fourier Transform

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Physical Review Applied 2023年第1期20卷 014010-014010页

作者： Jialong Zhu Yihua Wu Le Wang Hailong Zhou Shengmei Zhao Institute of Signal Processing and Transmission Nanjing University of Posts and Telecommunications (NUPT) Nanjing 210003 China School of Information Engineering Suqian University Suqian 223800 China Wuhan National Laboratory for Optoelectronics School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan 430074 China Key Lab of Broadband Wireless Communication and Sensor Network Technology (Nanjing University of Posts and Telecommunications) Ministry of Education Nanjing China National Laboratory of Solid State Microstructures Nanjing University Nanjing 210093 China

Measuring the orbital-angular-momentum (OAM) complex spectrum of light is very important in OAM-based applications. In this paper, we propose and experimentally demonstrate a simple scheme to measure the OAM complex spectrum of light with the fast Fourier transform (FFT). The light with input OAM modes coaxially interferes with a Gaussian light beam that is generated locally to achieve the intensity distribution of the interference. By subtracting the intensity distribution of the input OAM light from this interference intensity distribution, the OAM complex spectrum can be extracted by employing the FFT transform on the result of the subtraction. In addition, we also present an approach to enlarge the range of detectable OAM modes (from 1 to 50) and analyze the effects of optical alignment and the number of superimposed OAM modes on the measurement results. The proposed scheme is demonstrated in a proof-of-principle experiment, where six superimposed OAM modes can be measured without error, with their average deviation of amplitude and phase being 0.07 and 0.308, respectively. The proposed scheme can exploit applications in some advanced domains, including OAM sensing, OAM imaging, optical communication decoding, and various other OAM-based systems.

关键词： Angular momentum of light Metrology Spatial profiles of optical beams Optical sources & detectors Optical spectroscopy

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A Method of Shadowsocks(R) Traffic Identification Based on Protocol Analysis

A Method of Shadowsocks(R) Traffic Identification Based on P...

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International Conference on communication technology (ICCT)

作者： Jie Luo Liang Bao Lvlin Ni Science and Technology on Communication Security Laboratory The 30th Research Institute of China Electronics Technology Group Corporation Chengdu Sichuan China Key Lab of Information Network Security of Ministry of Public Security The Third Research Institute of Ministry of Public Security Shanghai China

ISBN: (纸本)9781665432078

Shadowsocks (R) is a proxy software based on Socks5, which is the collective name of Shadowsocks and Shadowsocks (R). In order to improves the shortcomings of other softwares breaking internet censorship, Shadowsocks (R) is a private protocol without handshake negotiation mechanism, and pays more attention to high camouflage and high concealment of traffic, which makes it difficult to use common methods(fingerprint identification, port filtering)to identify Shadowsocks (R) traffic[1], This paper proposes a method of Shadowsocks (R) traffic identification from the perspective of the protocol, which extracts features from four dimensions, and inputs features into the machine learning model for training. This method performs secondary identification and statistical filtering on the output of the machine learning model, and finally identifys Shadowsocks (R) traffic. This method has been very well validated in the laboratory environment.

关键词： Training Ports (computers) Protocols laboratories Machine learning Reconnaissance Feature extraction

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Deep high-order supervised hashing

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Optik 2019年 180卷 847-857页

作者： Jing Dong Cheng Qiu Le Sun Jian Xin Zhang Christian Desrosiers Bin Liu Jian Lu Qiang Zhang Key Lab of Advanced Design and Intelligent Computing (Ministry of Education) Dalian University Dalian 116622 China School of Information and Communication Engineering Dalian University of Technology Dalian 116024 China Laboratory for Imagery Vision and Artificial Intelligence (LIVAI) École de technologie supérieure (ETS) Montreal H3C1K3 Canada International School of Information Science and Engineering (DUT-RUISE) Dalian University of Technology Dalian 116622 China School of Computer Science and Technology Dalian University of Technology Dalian 116024 China

Recently, deep hashing has achieved excellent performance in large-scale image retrieval by simultaneously learning deep features and hash function. However, state-of-the-art methods for this task have so far failed to explore feature statistics higher than first-order. To address this problem, we propose two novel deep high-order supervised hashing (DHoSH) architectures based on point-wise labels (DHoSH-PO) and pair-wise labels (DHoSH-PA), respectively. The core of DHoSH is a trainable layer of bilinear features incorporated into existing deep convolutional neural network (CNN) for end-to-end learning. This layer captures the interaction of local features by bilinear pooling, using correlation to model dependencies of features within the same layer or cross-correlation for features across different layers of the CNN. Furthermore, DHoSH systematically employs the high-order statistics of features of multiple layers. Extensive experiments on commonly used image retrieval benchmarks show that our DHoSH-PO and DHoSH-PA models yield improved accuracy over its first-order counterparts and achieve effective performance for these benchmarks.

关键词： High-order statistics Supervised hashing Bilinear pooling CNN Image retrieval

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Deep Feature Surgery: Towards Accurate and Efficient Multi-Exit Networks

arXiv

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arXiv 2024年

作者： Gong, Cheng Chen, Yao Luo, Qiuyang Lu, Ye Li, Tao Zhang, Yuzhi Sun, Yufei Zhang, Le College of Software Nankai University China College of Computer Science Nankai University China National University of Singapore Singapore School of Information and Communication Engineering University of Electronic Science and Technology of China China HAIHE Lab of ITAI China Tianjin Key Laboratory of Network and Data Security Technology China Key Laboratory of Data and Intelligent System Security Ministry of Education

Multi-exit network is a promising architecture for efficient model inference by sharing backbone networks and weights among multiple exits. However, the gradient conflict of the shared weights results in sub-optimal accuracy. This paper introduces Deep Feature Surgery (DFS), which consists of feature partitioning and feature referencing approaches to resolve gradient conflict issues during the training of multi-exit networks. The feature partitioning separates shared features along the depth axis among all exits to alleviate gradient conflict while simultaneously promoting joint optimization for each exit. Subsequently, feature referencing enhances multi-scale features for distinct exits across varying depths to improve the model accuracy. Furthermore, DFS reduces the training operations with the reduced complexity of backpropagation. Experimental results on Cifar100 and ImageNet datasets exhibit that DFS provides up to a 50.00% reduction in training time and attains up to a 6.94% enhancement in accuracy when contrasted with baseline methods across diverse models and tasks. Budgeted batch classification evaluation on MSDNet demonstrates that DFS uses about 2× fewer average FLOPs per image to achieve the same classification accuracy as baseline methods on Cifar100. The code is available at https: //***/GongCheng1919/dfs. Copyright © 2024, The Authors. All rights reserved.

关键词： Budget control

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Defense technology of Wormhole Attacks Based on Node Connectivity

Defense Technology of Wormhole Attacks Based on Node Connect...

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IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity)

作者： Jiu-hu Zheng Huan-yan Qian Lei Wang School of Information Technology Jiangsu Institute of Commerce Nanjing Jiangsu China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing Jiangsu China National Mobile Communications esearch Laboratory Southeast University Nanjing Jiangsu China Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing Jiangsu China

ISBN: (纸本)9781509018949

After wormhole attacks the routing mechanism of wireless sensor network (WSN) is damaged, then the hop count and distance between nodes cannot be well matched, so that the WSN becomes the anisotropic sensor network, resulting in security treats for WSN. Considering that wormhole attacks can cause the changing of node connectivity, through the theoretical analysis and derivation, the paper has proposed a method which detects wormhole attacks with the connected relation of high connectivity nodes, and optimizes routing with the help of the normal nodes outside the wormhole. Simulation results show that the algorithm can well detect wormhole attacks and has better localization accuracy In addition, the effect is more obvious in the case of the wormhole nodes with large communication radius and long distance.

关键词： Wireless sensor networks Routing Probability Algorithm design and analysis Security Simulation Computer science

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DarkFed: A Data-Free Backdoor Attack in Federated Learning

arXiv

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arXiv 2024年

作者： Li, Minghui Wan, Wei Ning, Yuxuan Hu, Shengshan Xue, Lulu Zhang, Leo Yu Wang, Yichen School of Software Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

Federated learning (FL) has been demonstrated to be susceptible to backdoor attacks. However, existing academic studies on FL backdoor attacks rely on a high proportion of real clients with main task-related data, which is impractical. In the context of real-world industrial scenarios, even the simplest defense suffices to defend against the state-of-the-art attack, 3DFed. A practical FL backdoor attack remains in a nascent stage of development. To bridge this gap, we present DarkFed. Initially, we emulate a series of fake clients, thereby achieving the attacker proportion typical of academic research scenarios. Given that these emulated fake clients lack genuine training data, we further propose a data-free approach to backdoor FL. Specifically, we delve into the feasibility of injecting a backdoor using a shadow dataset. Our exploration reveals that impressive attack performance can be achieved, even when there is a substantial gap between the shadow dataset and the main task dataset. This holds true even when employing synthetic data devoid of any semantic information as the shadow dataset. Subsequently, we strategically construct a series of covert backdoor updates in an optimized manner, mimicking the properties of benign updates, to evade detection by defenses. A substantial body of empirical evidence validates the tangible effectiveness of DarkFed. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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DarkSAM: Fooling Segment Anything Model to Segment Nothing

arXiv

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arXiv 2024年

作者： Zhou, Ziqi Song, Yufei Li, Minghui Hu, Shengshan Wang, Xianlong Zhang, Leo Yu Yao, Dezhong Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM. Our codes are available at: https://***/CGCL-codes/DarkSAM. Copyright © 2024, The Authors. All rights reserved.

关键词： Frequency domain analysis

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