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检索条件"任意字段=IEEE International Conference on Signal Image Technology and Internet Based Systems"

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Two-Stream Decoder Feature Normality Estimating Network for Industrial Anomaly Detection

Two-Stream Decoder Feature Normality Estimating Network for ...

引用

international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Chaewon Park Minhyeok Lee Suhwan Cho Donghyeong Kim Sangyoun Lee Yonsei University Seoul Korea Korea Institute of Science and Technology

image reconstruction-based anomaly detection has recently been in the spotlight because of the difficulty of constructing anomaly datasets. These approaches work by learning to model normal features without seeing abnormal samples during training and then discriminating anomalies at test time based on the reconstructive errors. However, these models have limitations in reconstructing the abnormal samples due to their indiscriminate conveyance of features. Moreover, these approaches are not explicitly optimized for distinguishable anomalies. To address these problems, we propose a two-stream decoder network (TSDN), designed to learn both normal and abnormal features. Additionally, we propose a feature normality estimator (FNE) to eliminate abnormal features and prevent high-quality reconstruction of abnormal regions. Evaluation on a standard benchmark demonstrated performance better than state-of-the-art models.

关键词： Training signal processing Benchmark testing Feature extraction Acoustics Decoding Speech processing

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An Enhanced Dynamic image Watermarking Framework based on Phase Congruency and Saliency

An Enhanced Dynamic Image Watermarking Framework based on Ph...

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2021 international conference in Advances in Power, signal, and Information technology, APSIT 2021

作者： Nayak, Manas Ranian Sahoo, Dinak Kumar Sahoo, Jyoti Prakash Satapathy, Lalit Mohan Dev, Prabhu Prasad School of Computer Engineering India Bhubaneswar Engineering College Dept. of Computer Science and Engineering India ITER Dept of CSIT India

ISBN: (纸本)9781665425063

Perceptual watermarking approaches achieve better image fidelity with the advantages of the Human Visual System (HVS). Saliency uses the advantages of this feature by identifying different visually perceptual regions of an image. Additionally, Phase congruency (PC) exploits those features of digital images by detecting the corner points. In the proposed method, the information hiding Capacity is generated from both bit depth maps created by comparing phase congruency results and saliency outcomes of an image that ascertains the low perceptually visible pixels (utilizing the Least Significant Bit (LSB) technique). The key idea is to ensure an additional layer of security using Symmetric Key Cryptography (SKC) and to hide information in a less observant area that is not easily perceptible inside the cover image. The experimental result analysis ensures better imperceptibility and robustness as it resists different attacks along with superior data hiding capacity. © 2021 ieee.

关键词： image watermarking

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CheapNVS: Real-Time On-Device Narrow-Baseline Novel View Synthesis

CheapNVS: Real-Time On-Device Narrow-Baseline Novel View Syn...

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Konstantinos Georgiadis Mehmet Kerim Yucel Albert Saà-Garriga CERTH Information Technologies Institute Thessaloniki Greece Samsung R&D Institute UK (SRUK)

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Single-view novel view synthesis (NVS) is a notorious problem due to its ill-posed nature, and often requires large, computationally expensive approaches to produce tangible results. In this paper, we propose CheapNVS: a fully end-to-end approach for narrow baseline single-view NVS based on a novel, efficient multiple encoder/decoder design trained in a multi-stage fashion. CheapNVS first approximates the laborious 3D image warping with lightweight learnable modules that are conditioned on the camera pose embeddings of the target view, and then performs inpainting on the occluded regions in parallel to achieve significant performance gains. Once trained on a subset of Open images dataset, CheapNVS outperforms the state-of-the-art despite being 10× faster and consuming 6% less memory. Furthermore, CheapNVS runs comfortably in real-time on mobile devices, reaching over 30 FPS on a Samsung Tab 9+.

关键词： Performance evaluation Photography Three-dimensional displays Memory management signal processing Performance gain Cameras Real-time systems Mobile handsets Speech processing

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Detecting Abnormal and Dangerous Activities Using Artificial Intelligence on The Edge for Smart City Application 15

Detecting Abnormal and Dangerous Activities Using Artificial...

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15th international conference on Advanced Computing and Applications (ACOMP)

作者： Nhat Nam Tran Huu Linh Mai Thanh Vo Minh Int Univ Sch Elect Engn Ho Chi Minh City Vietnam

ISBN: (纸本)9781665406390

Artificial Intelligence (AI) and internet of Things (IoT) technologies have developed rapidly in recent years. AI on the Edge technology combined with IoT technology are very potential for smart city applications, the security protection is one of the very important problem in smart city. This study proposes a solution for detecting abnormal and dangerous activities using AI on the edge which can be applied in smart city applications. This project aims at developing a system which can detect abnormal and dangerous activities using Deep learning model on the edge computer. The video signal from the camera will be processed by embedded computer Jetson Nano, which is implemented with deep learning models to detect some abnormal and dangerous activities such as human without facemask in the SARS-CoV-2 pandemic areas or man with gun and knife in the city public areas..., the information of detected abnormal activities will be sent to cloud server through the IoT system. YOLOv5 deep learning model is selected to implement in this system, thousands of abnormal activities have been collected to train the model. A prototype abnormal and dangerous activities detection system has been designed and implemented in practical testing areas, which has very high accuracy detection result. based on these initial results of the proposed solution we can develop some practical applications for smart city to detect and track different kinds of abnormal human activities in smart city for security issues.

关键词： Deep learning Smart cities image edge detection Computational modeling Prototypes Security internet of Things

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Optimizing Key-Selection for Face-based One-Time Biometrics via Morphing

Optimizing Key-Selection for Face-Based One-Time Biometrics ...

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ieee international Workshop on Information Forensics and Security (WIFS)

作者： Dailé Osorio-Roig Mahdi Ghafourian Christian Rathgeb Ruben Vera-Rodriguez Christoph Busch Julian Fierrez Biometrics and Internet Security Research Group Hochschule Darmstadt Germany Biometrics and Data Pattern Analytics (BiDA) Lab Universidad Autonoma de Madrid (UAM) Spain

Nowadays, facial recognition systems are still vulnerable to adversarial attacks. These attacks vary from simple perturbations of the input image to modifying the parameters of the recognition model to impersonate an authorised subject. So-called privacy-enhancing facial recognition systems have been mostly developed to provide protection of stored biometric reference data, i.e. templates. In the literature, privacy-enhancing facial recognition approaches have focused solely on conventional security threats at the template level, ignoring the growing concern related to adversarial attacks. Up to now, few works have provided mechanisms to protect face recognition against adversarial attacks while maintaining high security at the template level. In this paper, we propose different key selection strategies to improve the security of a competitive cancelable scheme operating at the signal level. Experimental results show that certain strategies based on signal-level key selection can lead to complete blocking of the adversarial attack based on an iterative optimization for the most secure threshold, while for the most practical threshold, the attack success chance can be decreased to approximately 5.0%.

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Depth Completion Network Guided by RGB Attention 3

Depth Completion Network Guided by RGB Attention

引用

3rd ieee international conference on Frontiers technology of Information and Computer, ICFTIC 2021

作者： Liu, Kaixiang Hohai University College of Internet of Things Engineering Changzhou China

ISBN: (纸本)9781665406055

The goal of the depth completion task is to generate a dense depth map from a sparse depth map. The RGB image is usually introduced as a guide to help the network obtain the missing depth information. Due to the rich semantic features of RGB images, the method based on RGB guidance could obtain better performance. However, RGB guidance will bring wrong information in feature fusion, because the segmentation boundary of RGB features does not match the real depth distribution. A depth completion network based on the attention mechanism is proposed to correct the wrong fusion information. Bidirectional attention is introduced to correct wrong segmentation boundaries in RGB images. Compared with the SOTA methods, the proposed method still maintains excellent performance under different sparse input conditions. And the proposed method has shorter inference time and smaller model size, while obtaining competitive results. © 2021 ieee.

关键词： Semantics

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Dynamic Resource-Aware Federated Framework for Secure and Sustainable Learning in Data Sensitive Applications

Dynamic Resource-Aware Federated Framework for Secure and Su...

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international conference for Convergence of technology (I2CT)

作者： Manu Narula Jasraj Meena Dinesh Kumar Vishwakarma Department of Information Technology Delhi Technological University Delhi India School of Computer and System Sciences Jawaharlal Nehru University Delhi India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Federated Learning (FL) quickly gained popularity as a secure distributed learning technology based on insights sharing. The client-level privacy provided by FL has enabled its extensive integration in fields dealing with susceptible data such as E-commerce, the internet of Medical Things, etc. However, the transmitted insights may still be subjected to eavesdropping since the security of the transmission medium cannot be guaranteed. Using complex techniques, this can lead to the inference of the client data. In many cases, quantization is applied over transmitted payload to hinder such attempts but results in drop of accuracy in the global models. Moreover, the capabilities of client nodes are not considered in most implementations which may lead to under or over-utilization of client resources and cause client dropouts due to exhaustion. When combined, the quantization and client dropouts cause a significant performance loss and make the network less stable. In this paper, we propose a Dynamic Resource-Aware Federated Framework for Secure and Sustainable Learning, which layers the quantization process with an active workload management technique based on the capabilities of the client nodes. The framework minimizes the dropout rates significantly while recovering the accuracy lost in quantization to some extent. Through experiments compared against standard FL and Quantized FL over multiple datasets, the DRAFFSS shows gains in security over the former and in accuracy over the latter while keeping client dropouts negligible.

关键词： Training Quantization (signal) internet of Medical Things Dynamic scheduling Throughput Security Standards

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Blind Identification of Channel Codes under AWGN and Fading Conditions via Deep Learning

Blind Identification of Channel Codes under AWGN and Fading ...

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2022 international conference on Networking and Network Applications, NaNA 2022

作者： Peng, Haifeng Cao, Chunjie Sun, Yang Li, Haoran Wen, Xiuhua School of Cyberspace Security School of Cryptology Hainan University Haikou China Key Laboratory of Internet Information Retrieval of Hainan Province Hainan University Haikou China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

Blind identification of channel codes is crucial in intelligent communication and non-cooperative signal processing, and it plays a significant role in wireless physical layer security, information interception, and information confrontation. Previous researches show a high computation complexity by manual feature extractions, in addition, problems of indisposed accuracy and poor robustness are to be resolved in a low signal-to-noise ratio (SNR). For solving these difficulties, based on deep residual shrinkage network (DRSN), this paper proposes a novel recognizer by deep learning technologies to blindly distinguish the type and the parameter of channel codes without any prior knowledge or channel state, furthermore, feature extractions by the neural network from codewords can avoid intricate calculations. We evaluated the performance of this recognizer in AWGN, single-path fading, and multi-path fading channels, the results of the experiments showed that the method we proposed worked well. It could achieve over 85 % of recognition accuracy for channel codes in AWGN channels when SNR is not lower than 4dB, and provide an improvement of more than 5% over the previous research in recognition accuracy, which proves the validation of the proposed method. © 2022 ieee.

关键词： Deep learning

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Detection of Weak Small image Target based on Brain-Computer Interface 4

Detection of Weak Small Image Target Based on Brain-Computer...

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4th ieee international conference on Electronics technology, ICET 2021

作者： Xie, Fengyu Shen, Hui Yu, Yang Ye, Zeqi Hu, Dewen National University of Defense Technology College of Intelligence Science and Technology Changsha China

ISBN: (纸本)9781728176734

The paper proposes an innovative frame for enhanced detection of targets in images based on EEG. In this study, we have achieved the process of long-term EEG signal in single trial. We have designed the experimental paradigms and performed the experiment on 12 subjects. We analyzed the EEG signals by extracting dynamic features. We determined the target detection according to single-trial EEG, simultaneously the target position by eye movement signals. The results proved that the method we adopted is significantly useful and reached satisfactory accuracy. Furthermore, the research can be used as a supplementary means for target detection of machine learning. By the fusion of human experience and intelligence, this work breaks through some bottlenecks of traditional target detection, such as the detection of uncertain targets, small targets, severely distorted targets and incomplete targets. It can significantly improve the detection rate of image information and compatible migration capability. © 2021 ieee.

关键词： Eye movements

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Channel Estimation Using RIDNet Assisted OMP for Hybrid-Field THz Massive MIMO systems

Channel Estimation Using RIDNet Assisted OMP for Hybrid-Fiel...

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ieee international conference on Communications (ICC)

作者： Hasan Nayir Erhan Karakoca Ali Görçin Khalid Qaraqe Department of Electrical and Computer Engineering Texas A&M University at Qatar Doha Qatar Department of Electronics and Communication Engineering Istanbul Technical University İstanbul Turkey Communications and Signal Processing Research (HİSAR) Lab. TÜBİTAK BİLGEM Kocaeli Turkey Department of Electronics and Communication Engineering Yildiz Technical University İstanbul Turkey

The terahertz (THz) band radio access with larger available bandwidth is anticipated to provide higher capacities for next-generation wireless communication systems. However, higher path loss at THz frequencies significantly limits the wireless communication range. Massive multiple-input multiple-output (mMIMO) is an attractive technology to increase the Rayleigh distance by generating higher gain beams using low wavelength and highly directive antenna array aperture. In addition, both far-field and near-field components of the antenna system should be considered for modeling THz electromagnetic propagation, where the channel estimation for this environment becomes a challenging task. This paper proposes a novel channel estimation method using a real image denoising network (RIDNet) and orthogonal matching pursuit (OMP) for hybrid-field THz mMIMO channels, including far-field and near-field constituents. The simulation experiments are performed using the ray-tracing tool. The results demonstrate that the proposed RIDNet-based method consistently provides lower channel estimation errors than the conventional OMP algorithm for all signal-to-noise ratio (SNR) regions. The performance gap becomes higher at low SNR regimes. Furthermore, the results imply that the same error performance of the OMP can be achieved by the RIDNet-based method using a lower number of RF chains and pilot symbols.

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