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IEEE International Conference on Industrial Informatics (INDIN)

作者： Weipeng Cao Xuyang Yao Zhiwu Xu Yinghui Pan Yixuan Sun Dachuan Li Bohua Qiu Muheng Wei Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model's capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods.

关键词： Visualization Adaptation models Semantics Zero shot learning Neural networks Termination of employment Transformers Market research Sun Optimization

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Collaborative Edge Service Placement for Maximizing QoS with Distributed data Cleaning

Collaborative Edge Service Placement for Maximizing QoS with...

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International Workshop on Quality of Service

作者： Yuzhu Liang Wenhua Wang Xi Zheng Qin Liu Liang Wang Tian Wang School of Artificial Intelligence Beijing Normal University Beijing China School of Computing Macquarie University NSW Australia College of Computer Science and Electronic Engineering Hunan University Changsha China School of Computer Science Northwestern Polytechnical University Xi'an China BNU-UIC Institute of Artificial Intelligence and Future Networks Beijing Normal University Zhuhai China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai China

The proliferation of dirty data on Internet of Things (IoT) devices can undermine the accuracy of data-driven decision-making by affecting the distribution of original data. The Quality of Service (QoS) of data cleaning on these devices is heavily impacted by processing delay and accuracy. In this paper, we find that edge service placement is a key step aligned with data cleaning and consider the collaborative edge service placement with distributed data cleaning (SPDC) problem. To address this issue, we propose a novel distributed collaborative edge-based architecture that effectively balances the demands of storage, communication, computation, and load constraints. Experimental results show that the proposed approach significantly improves the accuracy of data cleaning by 0.31%-86.07% and reduces delay by 2.73%-58.71% compared to state-of-the-art baselines.

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Domain generalization for document authentication against practical recapturing attacks

arXiv

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arXiv 2021年

作者： Chen, Changsheng Zhang, Shuzheng Lan, Fengbo Huang, Jiwu The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security National Engineering Laboratory for Big Data System Computing Technology College of Electronics and Information Engineering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China

Recapturing attack can be employed as a simple but effective anti-forensic tool for digital document images. Inspired by the document inspection process that compares a questioned document against a reference sample, we proposed a document recapture detection scheme by employing Siamese network to compare and extract distinct features in a recapture document image. The proposed algorithm takes advantages of both metric learning and image forensic techniques. Instead of adopting Euclidean distance-based loss function, we integrate the forensic similarity function with a triplet loss and a normalized softmax loss. After training with the proposed triplet selection strategy, the resulting feature embedding clusters the genuine samples near the reference while pushes the recaptured samples apart. In the experiment, we consider practical domain generalization problems, such as the variations in printing/imaging devices, substrates, recapturing channels, and document types. To evaluate the robustness of different approaches, we benchmark some popular off-the-shelf machine learning-based approaches, a state-of-the-art document image detection scheme, and the proposed schemes with different network backbones under various experimental protocols. Experimental results show that the proposed schemes with different network backbones have consistently outperformed the state-of-the-art approaches under different experimental settings. Specifically, under the most challenging scenario in our experiment, i.e., evaluation across different types of documents that produced by different devices, we have achieved less than 5.00% APCER (Attack Presentation Classification Error Rate) and 5.56% BPCER (Bona Fide Presentation Classification Error Rate) by the proposed network with ResNeXt101 backbone at 5% BPCER decision *** Codes 94A08 Copyright © 2021, The Authors. All rights reserved.

关键词： Feature extraction

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A Novel Statistical Preprocessing Approach for Hyperspectral Image Unmixing

A Novel Statistical Preprocessing Approach for Hyperspectral...

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Remote Sensing Symposium (InGARSS), 2020 IEEE India Geoscience and

作者： Fatemeh Kowkabi Ahmad Keshavarz Lalit Kumar College of Engineering Marvdasht Branch Islamic Azad University Marvdasht Iran IoT and Signal Processing Research Group ICT Research Institute Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr Iran EastCoast Geospatial Consultants Armidale NSW Australia

ISBN: (纸本)9781665442503

Hyperspectral images have wide application in different commercial and military missions. However, these images suffer from the low spatial resolution, and hence, spectral unmixing has been proposed as a suitable technique to solve this deficiency. Recently, some preprocessing modules have been proposed which use the combination of spatial and spectral information of image pixels with the purpose of improving endmember extraction and spectral unmixing in hyperspectral images. In this paper, we propose a statistical preprocessing algorithm using weighted extended cumulative entropy (WECE) as the Edge Detection algorithm to identify spectrally pure pixels and KL distance estimation using the mean and covariance values of pure oversegments and their neighbors to find the spatially homogenous and spectrally pure pixels for the next endmember extraction algorithms. Moreover, the average of KL distances are applied due to the reduction of noise power and local spectral variability. The performance of the novel statistical preprocessing algorithm has been compared with the sate-of-the-art in terms of RMSE reconstruction and average SAD errors on two real hyperspectral images known as HYDICE urban and AVIRIS Indian Pines in combination with several endmember extraction (EE) algorithms and its efficiency is evaluated.

关键词： Image edge detection Geoscience and remote sensing Estimation Entropy data mining Spatial resolution Image reconstruction

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A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation

A Semi-Supervised Approach with Error Reflection for Echocar...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaoxiang Han Yiman Liu Jiang Shang Qingli Li Jiangang Chen Menghan Hu Qi Zhang Yuqi Zhang Yan Wang School of Communication and Information Engineering Shanghai University Shanghai P.R.China The SMART (Smart Medicine and AI-based Radiology Technology) Lab Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai P.R.China Department of Pediatric Cardiology Shanghai Children’s Medical Center School of Medicine Shanghai Jiao Tong University Shanghai P.R.China Shanghai Engineering Research Center of Intelligence Pediatrics (SERCIP) Shanghai P.R.China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai P.R.China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Segmenting internal structure from echocardiography is essential for the diagnosis and treatment of various heart diseases. Semi-supervised learning shows its ability in alleviating annotations scarcity. While existing semi-supervised methods have been successful in image segmentation across various medical imaging modalities, few have attempted to design methods specifically addressing the challenges posed by the poor contrast, blurred edge details and noise of echocardiography. These characteristics pose challenges to the generation of high-quality pseudo-labels in semi-supervised segmentation based on Mean Teacher. Inspired by human reflection on erroneous practices, we devise an error reflection strategy for echocardiography semi-supervised segmentation architecture. The process triggers the model to reflect on inaccuracies in unlabeled image segmentation, thereby enhancing the robustness of pseudo-label generation. Specifically, the strategy is divided into two steps. The first step is called reconstruction reflection. The network is tasked with reconstructing authentic proxy images from the semantic masks of unlabeled images and their auxiliary sketches, while maximizing the structural similarity between the original inputs and the proxies. The second step is called guidance correction. Reconstruction error maps decouple unreliable segmentation regions. Then, reliable data that are more likely to occur near high-density areas are leveraged to guide the optimization of unreliable data potentially located around decision boundaries. Additionally, we introduce an effective data augmentation strategy, termed as multi-scale mixing up strategy, to minimize the empirical distribution gap between labeled and unlabeled images and perceive diverse scales of cardiac anatomical structures. Extensive experiments on a public echocardiography dataset CAMUS, and a private clinical echocardiography dataset demonstrate the competitiveness of the proposed method.

关键词： Image segmentation Echocardiography Noise Semantics Anatomical structure Semisupervised learning data augmentation Reflection Robustness Image reconstruction

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Deep learning-based forgery attack on document images

arXiv

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arXiv 2021年

作者： Zhao, Lin Chen, Changsheng Huang, Jiwu the Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security National Engineering Laboratory for Big Data System Computing Technology College of Electronics and Information Engineering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China

With the ongoing popularization of online services, the digital document images have been used in various applications. Meanwhile, there have emerged some deep learning-based text editing algorithms which alter the textual information of an image in an end-to-end fashion. In this work, we present a low-cost document forgery algorithm by the existing deep learning-based technologies to edit practical document images. To achieve this goal, the limitations of existing text editing algorithms towards complicated characters and complex background are addressed by a set of network design strategies. First, the unnecessary confusion in the supervision data is avoided by disentangling the textual and background information in the source images. Second, to capture the structure of some complicated components, the text skeleton is provided as auxiliary information and the continuity in texture is considered explicitly in the loss function. Third, the forgery traces induced by the text editing operation are mitigated by some post-processing operations which consider the distortions from the print-and-scan channel. Quantitative comparisons of the proposed method and the exiting approach have shown the advantages of our design by reducing the about 2/3 reconstruction error measured in MSE, improving reconstruction quality measured in PSNR and in SSIM by 4 dB and 0.21, respectively. Qualitative experiments have confirmed that the reconstruction results of the proposed method are visually better than the existing approach in both complicated characters and complex texture. More importantly, we have demonstrated the performance of the proposed document forgery algorithm under a practical scenario where an attacker is able to alter the textual information in an identity document using only one sample in the target domain. The forged-and-recaptured samples created by the proposed text editing attack and recapturing operation have successfully fooled some existing document authenticat

关键词： Textures

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GAN-based Technology of Generating Spoofing-jamming I/Q Signal

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Journal of Computers (Taiwan) 2022年第4期33卷 133-144页

作者： Xu, Rui-Ze Hu, Sheng-Bo Lv, Ye Zhu, Man-Qin Meng, Xin School of Big Data and Computer Science Guizhou Normal University Guizhou Guiyang550001 China Institute of Intelligent Information Processing Guizhou Normal University Guizhou Guiyang550001 China Center for RFID and WSN Engineering Department of Education Guizhou Guizhou Guiyang550001 China School of Mathematical Sciences Guizhou Normal University Guizhou Guiyang550001 China

The existing wireless communication interference methods rely heavily on the characteristics of the target signals obtained in communication reconnaissance, require complex prior knowledge, and have problems in keeping up with the dynamic changes of relevant parameters. This paper proposes a GAN-based technology of generating spoofing-jamming I/Q signals. The jammer will be able to deceive and interfere with op-ponents through GAN by generating spoofing-jamming I/Q signals highly correlated to real I/Q signals without prior knowledge. This paper first introduces the principle of GAN and the GAN model to be adopted;uses a software radio system composed of LabView and NI USRP software and hardware plat-forms to simulate real communication scenarios to collect real I/Q signal data;generates spoofing-jamming I/Q signals through GAN model;uses t-SNE algorithm to perform dimensionality reduction on both the real and the spoofing-jamming I/Q signal data to visualize their distributions;finally, tests the spoofing-jamming I/Q signals on receiver's pre-trained classifier. The experimental results show that GAN provides an effective method for generating high-quality spoofing-jamming I/Q signals. © 2022 Computer Society of the Republic of China. All rights reserved.

关键词： Signal receivers

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Wideband polarization reconfigurable antenna based on double C-shaped radiators

Wideband polarization reconfigurable antenna based on double...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Weilong Liang Long Li Zhao Wu Yanhu Huang Qingxiu Ma Jiajun Liang Nanjing Research Institute of Electronics Technology Nanjing P. R. China School of Physics and Telecommunication Engineering Yulin Normal University Yulin P. R. China Guangxi Colleges and Universities Key Lab of Complex System Optimization and Big Data Processing Yulin Normal University Yulin P. R. China

An enhancing bandwidth tri-polarization reconfigurable antenna based on double C-shaped radiators is proposed. The antenna consists of a modified ground plane, double C-shaped radiators, and a feedline with a simple parasitic stub. The modified ground plane and the parasitic stub can improve the impedance matching, meanwhile, adjust the axial ratio performance. Two PIN diodes are embedded between the C-shaped radiator and the feedline. Three polarization reconfigurable modes can be achieved caused by switching two PIN diodes state, linear polarization (LP), left-hand circular polarization (LHCP), and right-hand circular polarization (RHCP). As the simulated results show, the −10-dB impedance bandwidth reaches 126.6% (1.76-7.83 GHz) in LP mode and 78.4% (2.96-6.78 GHz) in CP modes. And the proposed antenna has a 3-dB axial ratio (AR) bandwidth of 84% (2.6-6.4 GHz) in two CP modes. Therefore, the proposed antenna can meet the requirement of the modern wireless and satellite communication system.

关键词： Wireless communication Polarization Satellite antennas PIN photodiodes Switches Antenna feeds Broadband antennas

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Modeling, Capacity Studies, Antenna and System Designs for 6G/B6G 3D Continuous-Space Radio Channels Enabled by Electromagnetic Information Theory

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IEEE Communications Surveys and Tutorials 2025年

作者： Wang, Cheng-Xiang Li, Junling Huang, Jie Huang, Chen Zhang, Zhaoyang Liu, Ying Zhou, Shidong Chen, Yunfei You, Xiaohu Gao, Xiqi Cui, Tie Jun Renzo, Marco Di Thompson, John Haas, Harald Schober, Robert Zhang, Rui Southeast University National Mobile Communications Research Laboratory School of Information Science and Engineering Nanjing211189 China Purple Mountain Laboratories Nanjing211111 China Zhejiang University Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking College of Information Science and Electronic Engineering Hangzhou310007 China Xidian University National Key Laboratory of Radar Detection and Sensing Xi'an710071 China Tsinghua University State Key Laboratory on Microwave and Digital Communications Beijing100084 China University of Durham Department of Engineering DurhamDH1 3LE United Kingdom Southeast University State Key Laboratory of Millimeter Waves School of Information Science and Engineering Nanjing211189 China UniversitèParis-Saclay Laboratoire des Signaux et Systèmes CNRS CentraleSupélec Gif-sur-Yvette91192 France Centre for Telecommunications Research - Department of Engineering King's College London LondonWC2R 2LS United Kingdom School of Engineering EdinburghEH9 3BF United Kingdom Cambridge University Department of Engineering CambridgeCB2 1PZ United Kingdom Institute for Digital Communications Erlangen91054 Germany The Chinese University of HongKong School of Science and Engineering Shenzhen Research Institute of Big Data Shenzhen518172 China National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore

Channel theory is a fundamental theory of wireless communications. The sixth generation (6G) and beyond 6G (B6G) wireless communication networks are expected to provide space-air-ground-sea global coverage. Base stations and users tend to move in three dimensional (3D) continuous spaces, while antennas and propagation environments can be tightly coupled. The underlying channels show an evolutionary trend to 3D continuous-space radio channels that combine antennas and wireless propagation channels, in comparison to discrete local-space wireless propagation channels in previous generations. This introduces new challenges for channel modeling, channel capacity analysis, antenna design, system design, etc. To address these challenges, this paper performs a comprehensive study on 3D continuous-space radio channels in 6G/B6G with the aid of electromagnetic information theory (EIT) that integrates electromagnetic theory, information theory, wireless propagation channel modeling theory, and antenna theory. We start by revealing the connections and gaps between these four fundamental theories. Then, an in-depth investigation on the four major research thrusts of 3D continuous-space radio channels is provided: 1) channel measurements and modeling, 2) channel capacity analysis, 3) general antenna design, and 4) wireless system design. We aim to explore the intrinsic relationships between antenna parameters, channel parameters, channel characteristics, channel capacity, and communication system performance. Finally, future research directions and challenges for 3D continuous-space radio channels are outlined. Our study endeavors to establish a fundamental framework for 3D continuous-space radio channels, with the potential to catalyze breakthroughs in 6G/B6G theories. © 1998-2012 IEEE.

关键词： Antenna grounds

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GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross Appearance-Edge Learning

arXiv

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arXiv 2024年

作者： Zhang, Yaning Yu, Zitong Wang, Tianyi Huang, Xiaobin Shen, Linlin Gao, Zan Ren, Jianfeng Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Nanyang Technological University 50 Nanyang Ave Block N 4 639798 Singapore Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518129 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University China Jinan250014 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China School of Computer Science University of Nottingham Ningbo China

The rapid advancement of photorealistic generators has reached a critical juncture where the discrepancy between authentic and manipulated images is increasingly indistinguishable. Thus, benchmarking and advancing techniques detecting digital manipulation become an urgent issue. Although there have been a number of publicly available face forgery datasets, the forgery faces are mostly generated using GAN-based synthesis technology, which does not involve the most recent technologies like diffusion. The diversity and quality of images generated by diffusion models have been significantly improved and thus a much more challenging face forgery dataset shall be used to evaluate SOTA forgery detection literature. In this paper, we propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection, which contains a large number of forgery faces generated by advanced generators such as the diffusion-based model and more detailed labels about the manipulation approaches and adopted generators. In addition to evaluating SOTA approaches on our benchmark, we design an innovative Cross Appearance-Edge Learning (CAEL) detector to capture multi-grained appearance and edge global representations, and detect discriminative and general forgery traces. Moreover, we devise an Appearance-Edge Cross-Attention (AECA) module to explore the various integrations across two domains. Extensive experiment results and visualizations show that our detection model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. Code and datasets will be available at https://***/Jenine-321/GenFace. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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