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arXiv 2023年

作者： Tan, Chuangchuang Liu, Huan Zhao, Yao Wei, Shikui Gu, Guanghua Liu, Ping Wei, Yunchao Institute of Information Science Beijing Jiaotong University China Beijing Key Laboratory of Advanced Information Science and Network Technology China School of Information Science and Engineering Yanshan University China Hebei Key Laboratory of Information Transmission and Signal Processing China Center for Frontier AI Research IHPC A*STAR Singapore

Recently, the proliferation of highly realistic synthetic images, facilitated through a variety of GANs and Diffusions, has significantly heightened the susceptibility to misuse. While the primary focus of deepfake detection has traditionally centered on the design of detection algorithms, an investigative inquiry into the generator architectures has remained conspicuously absent in recent years. This paper contributes to this lacuna by rethinking the architectures of CNN-based generator, thereby establishing a generalized representation of synthetic artifacts. Our findings illuminate that the up-sampling operator can, beyond frequency-based artifacts, produce generalized forgery artifacts. In particular, the local interdependence among image pixels caused by upsampling operators is significantly demonstrated in synthetic images generated by GAN or diffusion. Building upon this observation, we introduce the concept of Neighboring Pixel Relationships(NPR) as a means to capture and characterize the generalized structural artifacts stemming from up-sampling operations. A comprehensive analysis is conducted on an open-world dataset, comprising samples generated by 28 distinct generative models. This analysis culminates in the establishment of a novel state-of-the-art performance, showcasing a remarkable 11.6% improvement over existing methods. The code is available at https://***/chuangchuangtan/NPRDeepfakeDetection. Copyright © 2023, The Authors. All rights reserved.

关键词： Network architecture

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Polarization-Guided Strategy for Ship Detection in Single-Polarization SAR Images

Polarization-Guided Strategy for Ship Detection in Single-Po...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jian Guan Haotian Yuan Pengming Feng Guangjun He Shichao Jin Group of Intelligent Signal Processing College of Computer Science and Technology Harbin Engineering University Harbin China Modeling and Emulation in E-Government National Engineering Laboratory Harbin Engineering University Harbin China State Key Laboratory of Space-Ground Integrated Information Technology Beijing China

High-performance target detection algorithms have been proposed for ship detection in Synthetic Aperture Radar (SAR) images in recent years. However, most of them are applied in the situation of single-polarization SAR images and rarely consider the important polarization information in SAR images. In this paper, a polarization-guided strategy is presented to improve the detection performance in single polarization SAR images by predicting polarization type. In which, an extra polarization-guided head is employed following the backbone network to guide the feature maps from the backbone to explore polarization information. Then, the feature map with the polarization information is fused with those from backbone network to enhance feature extraction in feature pyramid networks (FPN), and yields better detection performance. Experiments conducted demonstrate the effectiveness of the proposed method, which can be easily merged with different detectors.

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Novel Off-Grid Estimation Method for Direction-of-Arrival Estimation Based on Deep Learning

Novel Off-Grid Estimation Method for Direction-of-Arrival Es...

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International Conference on Microwave and Millimeter Wave technology Proceedings

作者： Jun Zhang Yanjun Zhang Jun Tao Jiang Zhu Zhanye Chen Yan Huang North electronics research institude co. LTD Key Laboratory of Underwater Acoustic Signal Processing of the Ministry of Education School of Information Science and Engineering Southeast University Nanjing China Xi'an Institute of Space Radio Technology Xi'an China State Key Laboratory of Millimeter Waves School of Information Science and Engineering Southeast University Nanjing China

ISBN: (数字)9798350389968

ISBN: (纸本)9798350389975

Direction of arrival (DOA) estimation has always been an important and widely studied topic. In practical scenarios, achieving higher resolution and timely response speed is always a priority. Incorporating deep learning (DL) techniques into DOA estimation confers a multitude of advantages, such as efficient computation time. Nonetheless, similar to traditional algorithms, they encounter challenges with grid resolution limitations. This paper introduces a novel deep learning framework for accurately estimating DOA of off-grid signals through regression task, effectively addressing grid mismatch issues. Experimental results confirm the enhanced efficacy of the proposed method under various scenarios, surpassing traditional and other DL methods.

关键词： Deep learning Microwave technology Microwave integrated circuits Direction-of-arrival estimation Accuracy Millimeter wave technology Estimation

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ZCS-CDiff: A Zero-Shot Code-Switching TTS System with Conformer-Based Diffusion Model

ZCS-CDiff: A Zero-Shot Code-Switching TTS System with Confor...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Ke Chen Zhihua Huang Liang He Yonghong Yan School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China Department of Electronic Engineering Tsinghua University Beijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of Speech Acoustics and Content Understanding Institute of Acoustics CAS Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Code-Switching (CS) Text-To-Speech (TTS) models have gained attention due to the increasing prevalence of multilingual communication. However, existing models struggle to meet the growing demand for personalized CS TTS, particularly in Zero-Shot (ZS) scenarios where the model should generate speech for an unseen speaker from reference speech. In this paper, we propose ZCS-CDiff, a zero-shot code-switching TTS system with the Conformer-based diffusion model. We disentangle speech features and use the diffusion model to precisely model these disentangled attributes, resulting in high-quality ZS CS speech. Additionally, we introduce a Conformer-based WaveNet as the denoising network within the diffusion model to further improve the accuracy of modeling different attributes. We also designed a speaker-assist module to help the model better handle speaker information extracted from the reference speech, resulting in CS speech with higher speaker similarity. Experimental results and ablation studies demonstrate that the ZS CS speech generated by ZCS-CDiff has good speech naturalness, intelligibility, and speaker similarity while confirming the effectiveness of our design choices. Audio samples are available 1 .

关键词： Training Adaptation models Accuracy Speech coding Noise reduction Speech enhancement signal processing Diffusion models Probabilistic logic Text to speech

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Robust multiple model-based control proposed for a SISO CSTR Benchmark 2

Robust multiple model-based control proposed for a SISO CSTR...

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2nd IEEE International Conference on signal, Control and Communication, SCC 2021

作者： Yousfi, Marwa Garna, Tarek Messaoud, Hassani University of Monastir Research Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir Monastir5019 Tunisia University of Sousse Higher Institute of Applied Science and Technology of Sousse Sousse4003 Tunisia

ISBN: (纸本)9781665402705

In this paper, robust multiple model-based LSDP (Loop-Shaping Design Procedure) controller is proposed for a SISO system where each linear submodel is represented in the form of a SISO ARX (Auto Regressive with exogenous input) Laguerre model. The proposed controller guarantees robustness in term of stability and reference tracking of the SISO system in presence of parametric uncertainties. Simulation example on the SISO CSTR (Continuous Stirred Tank Reactor) Benchmark is given to show the efficiency and the robustness of the proposed controller in handling the parametric uncertainties of the process. © 2021 IEEE.

关键词： Controllers

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A Fast Method for Shortest-Path Cover Identification in Large Complex Networks

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Computers, Materials & Continua 2020年第5期63卷 705-724页

作者： Qiang Wei Guangmin Hu Chao Shen Yunfei Yin School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu611731China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu610041China MOE Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong UniversityXi’an710049China The Electronic Engineering Department Universidad de SevillaSeville41004Spain School of Astronautics Harbin Institute of TechnologyHarbin150001China

Fast identifying the amount of information that can be gained by measuring a network via shortest-paths is one of the fundamental problem for networks exploration and ***,the existing methods are time-consuming for even moderate-scale *** this paper,we present a method for fast shortest-path cover identification in both exact and approximate scenarios based on the relationship between the identification and the shortest distance *** effectiveness of the proposed method is validated through synthetic and real-world *** experimental results show that our method is 105 times faster than the existing methods and can solve the shortest-path cover identification in a few seconds for large-scale networks with millions of nodes and edges.

关键词： Network discovery shortest-path cover shortest-path distance query large complex networks

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Deep Learning in the Field of Biometric Template Protection: An Overview

arXiv

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arXiv 2023年

作者： Rathgeb, Christian Kolberg, Jascha Uhl, Andreas Busch, Christoph Da/sec – Biometrics and Internet Security Research Group Hochschule Darmstadt Germany WaveLab – Multimedia Signal Processing and Security Lab Universität Salzburg Austria Norwegian Biometrics Laboratory Norwegian University of Science and Technology Norway

Today, deep learning represents the most popular and successful form of machine learning. Deep learning has revolutionised the field of pattern recognition, including biometric recognition. Biometric systems utilising deep learning have been shown to achieve auspicious recognition accuracy, surpassing human performance. Apart from said breakthrough advances in terms of biometric performance, the use of deep learning was reported to impact different covariates of biometrics such as algorithmic fairness, vulnerability to attacks, or template protection. Technologies of biometric template protection are designed to enable a secure and privacy-preserving deployment of biometrics. In the recent past, deep learning techniques have been frequently applied in biometric template protection systems for various purposes. This work provides an overview of how advances in deep learning take influence on the field of biometric template protection. The interrelation between improved biometric performance rates and security in biometric template protection is elaborated. Further, the use of deep learning for obtaining feature representations that are suitable for biometric template protection is discussed. Novel methods that apply deep learning to achieve various goals of biometric template protection are surveyed along with deep learning-based attacks. Copyright © 2023, The Authors. All rights reserved.

关键词： Biometrics

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An Overview of Organs-on-Chips Based on Deep Learning

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Research 2022年第2期2022卷 141-160页

作者： Jintao Li Jie Chen Hua Bai Haiwei Wang Shiping Hao Yang Ding Bo Peng Jing Zhang Lin Li Wei Huang Frontiers Science Center for Flexible Eletronrics Xi'an Institu te of Flexible Electronics(IFE)and Xr'an Institute of Biomedical Materials EngineeringNorthwesterm Polytechnical UniversityXi'an 710072China Key Laboratory of Ietlieant Computing and Signal Processing of Minsry of Educaton School of Electronics and Information EngineeringAnhui UriversityHefei 230601China 38th Research Institute of China Eilectronics Technology Grouep Corporation Hiefei 230088 China Collge of Biomedical Engimeering Sichuan UntiversityChengdu 610065China Key Laboratory of Flexible Elecronics(KLOFE)and Institute of Advanced Materials(IAM)Nanjing Tech University(NanjingTech)J Nanjing 211800 China

Microfluidic-based organs-on-chips(OoCs)are a rapidly developing technology in biomedical and chemical research and have emerged as one of the most advanced and promising in vitro *** miniaturization,stimulated tissue mechanical *** microenvironment of OoCs offer unique properties for biomedical ***,the large amount of data generated by the high parallelization of OoC systems has grown far beyond the scope of manual analysis by researchers with biomedical *** learning,an emerging area of research in the field of machine learning,can automatically mine the inherent characteristics and laws of"big data"and has achieved remarkable applications in computer vision,speech recognition,and natural language *** integration of deep learning in OoCs is an emerging field that holds enormous potential for drug development,disease modeling,and personalized *** review bricfly describes the basic concepts and mechanisms of microfluidics and deep learning and summarizes their successful *** then analyze thecombination of OoCs and deep learning for image digitization,data analysis,and ***,the problems faced in current applications are discussed,and future perspectives and suggestions are provided to further strengthen this integration.

关键词： Deep holds beyond

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Passive Localization of Wideband Near-Field Sources Using Sparse Bayesian Learning and Envelope Alignment

SSRN

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SSRN 2023年

作者： Zhang, Xiaoyu Tao, Haihong Xie, Jian Fang, Ziye National Laboratory of Radar Signal Processing Xidian University Xi’an710071 China School of Electronics and Information Northwestern Polytechnical University Xi’an710072 China School of Computer Science and Technology Xidian University Xi’an710071 China

Most studies about passive localization of near-field signals are limited to narrowband sources. In this paper, a novel efficient algorithm is developed for direction of arrival (DOA) and range estimation of wideband near-field sources. Using the specific structure of symmetric linear array, the direction information is extracted and the off-grid Sparse Bayesian learning (SBL) framework is established for DOA estimation. The generalized approximate message passing (GAMP) strategy is applied for fast implementation. Then the envelope aligned approach is proposed for the range estimation in time domain and the linear searching concept is applied to accelerate the computation. Compared with the existing wideband near-field localization methods, the proposed algorithm has better performance of accuracy and resolution probability. Simulation results validate the feasibility and effectiveness of the proposed algorithm. © 2023, The Authors. All rights reserved.

关键词： Message passing

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Research on patent classification based on hierarchical label semantics

Research on patent classification based on hierarchical labe...

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Education, Knowledge and Information Management (ICEKIM), International Conference on

作者： Jie Chen Shu Zhao Fan Lu Feng Liu Yangping Zhang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China School of Computer Science and Technology Anhui University Hefei China

ISBN: (纸本)9781665416672

Patent classification is an essential task in patent information management and knowledge mining. Most existing studies are based on the textual content of individual patent texts (e.g., titles and abstracts) for classification, but the patent also has label information with hierarchical structure and semantic description. However, the semantics correlation of patent texts and their labels have been largely ignored. In this paper, we propose a novel framework called HLSPC for patent classification by leveraging the hierarchical semantics correlation of patent texts and their labels. Specifically, we first apply a patent representation learning module for capturing the semantics representation of patent texts and hierarchical labels. Then, we design a label attention learning module to build the semantics correlation between patent texts and hierarchical labels, which enhances patent representation. Finally, we deploy a multi-level fusion module to get the refined category prediction for each patent which can preserve both local and global hierarchical prediction information. Extensive experimental results on two English patent datasets demonstrate the effective power of the HLSPC model.

关键词： Representation learning Patents Correlation Semantics Text categorization Education Information management

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