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Demystifying deepfakes using deep learning

Demystifying deepfakes using deep learning

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International conference on Computing Methodologies and Communication (ICCMC)

作者： Raj Kumar Singh Prachi Vinod Sarda Shruti Aggarwal Dinesh Kumar Vishwakarma Delhi Technological University New Delhi India

ISBN: (纸本)9781665403603;9781665447751

Manipulation of images, videos and audios using face edit apps and web services have long been in use, since decades but recent advances in deep learning has led to rising AI generated fake images and videos with swapped faces, lip synced audios and puppet masters, popularly known as Deepfakes. Generated primarily using one of the following two approaches namely, Autoencoders and Generator Adversarial Networks which rests on trained deep neural networks, deepfakes offer unprecedented challenges. the degree of realism achieved by deep learning powered deepfakes increases with increasing amounts of data i.e, fake images and videos readily available on the internet at disposal to train GANs. Deepfake algorithms create media leaving a bare margin of difference between the authentic or original source and the forged or deepfaked targets. thus, new mechanisms and techniques to detect and filter out such deepfakes is the need of the *** paper exploits two powerful deep learning based CNN architectures namely, Inception-Resnet-v2 and XceptionNet for detecting the deepfakes. the proposed approach not only outshines the existing approaches in terms of efficiency and accuracy but also offers the best in terms of the given space and time complexity.

关键词： Deep learning Web services Lips Neural networks Media Information filters Time complexity

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Locomotive number location based on Retinex image enhancement and improved SWT algorithm

Locomotive number location based on Retinex image enhancemen...

引用

IEEE Advanced Information Technology, Electronic and Automation Control conference (IAEAC)

作者： Junfeng Cui China Railway No.3 Engineering Group Jinzhong China

ISBN: (数字)9781728180281

ISBN: (纸本)9781728180298

In order to solve the problems of poor robustness and low real-time performance of traditional algorithms for locomotive number location, a vehicle number location method based on Retinex image enhancement and improved stroke width transformation algorithm is proposed. Retinex algorithm is used to enhance the image edge information, and the edge detection and maximum stable extremum region algorithm are used to roughly locate the train number. then, the improved stroke width transformation algorithm is used to calculate the stroke width and stroke gradient, and the threshold value is set to limit the search range of pixels to avoid global search and improve the timeliness of the algorithm. the experimental results show that compared with the existing algorithms, the positioning accuracy of the proposed algorithm is improved by 4.72%, the time complexity is reduced by 45.03% compared with the traditional stroke width transformation algorithm, and it also shows good positioning effect when applied to other scenes.

关键词： Image edge detection Heuristic algorithms Lighting Robustness Real-time systems Time complexity Image enhancement

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Reinforcement Learning-based Computation Resource Allocation Scheme for 5G Fog-Radio Access Network 5

Reinforcement Learning-based Computation Resource Allocation...

引用

5th International conference on Fog and Mobile Edge Computing (FMEC)

作者： Khumalo, Nosipho Oyerinde, Olutayo Mfupe, Luzango Univ Witwatersrand Sch Elect & Informat Engn Johannesburg South Africa CSIR Next Generat Enterprises & Inst Pretoria South Africa

ISBN: (纸本)9781728172163

Fog computing has emerged as one of the key building blocks of fifth generation mobile networks (5G) because of its ability to effectively meet the demands of real-time or latency-sensitive applications. To introduce fog in 5G, particularly in the radio access network (RAN), intermediate network devices such as remote radio heads, small cells and macro cells are equipped with virtualised storage and processing resources to constitute the fog RAN (F-RAN). However, these resources are limited and inefficient management could cause a bottleneck for F-RAN nodes. To this end, this paper focuses on developing a dynamic and autonomous computing resource allocation scheme for F-RAN considering delay requirements of users at a node. the proposed algorithm uses reinforcement learning to optimise latency, energy consumption and cost in the F-RAN. the performance and computational complexity of the proposed algorithm will be evaluated as part of a simulation and the results compared with other algorithms from existing studies with a similar objective function.

关键词： fog computing 5G RAN reinforcement learning edge computing machine learning

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Data-driven Based Technique for Joint Power Allocation and User Pairing in NOMA System

Data-driven Based Technique for Joint Power Allocation and U...

引用

Japan-Egypt International conference on Electronics, Communications and Computers (JEC-ECC)

作者： Donia Mohamed Ahmed Magdy Osama Elghandour Ahmed Nasser The Higher Institute of Engineering and Technology in 5th Settlement University Egypt Faculty of Engineering Suez Canal University Egypt Faculty of Engineering Helwan University Egypt

ISBN: (纸本)9781665482936

As one of the proposed multiple access techniques of the fifth-generation and beyond systems, NOMA, which stands for non-orthogonal multiple access. NOMA is a power domain access technique, which ensures better spectrum efficiency and system capacity. However, the representation of NOMA is challenging because of inter-user interference and the complexity of successive interference cancellation (SIC) in the receiver. We suggest a low-complexity power allocation (PA) and user pairing (UP) scheme using a model-free technique that depends on the real-time input and output calculations, data-driven (DD), where the near-optimal solution has been found. So, data-driven can be utilized as an optimization tool for finding a near-optimum solution of the original NP-hard problem of joint PA and UP. Analytical and simulation results referred that the proposed algorithm can enhance spectrum efficiency compared with the existing algorithms.

关键词： NOMA Interference cancellation Silicon carbide NP-hard problem Simulation Receivers Real-time systems

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Glaucoma Detection using Mask Region-Based Convolutional Neural Networks

Glaucoma Detection using Mask Region-Based Convolutional Neu...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Akanksha Singh Patel Varun Singh Rewa Institute of Technology Rewa India

ISBN: (纸本)9781665435253

Glaucoma is a disease that impairs the vision of the human eyes, and it has grown increasingly common in recent years due to increased pressure in the eyes. It is the result of this disease, which is fatal once it has shown itself, that vision loss occurs. the identification of glaucoma disease has already been achieved via several deep learning (DL) algorithms, as previously stated. the findings of our research on the detection of glaucoma sickness in this paper, in which model used a deep learning technique known as a Convolutional neural network to detect the illness (CNN). the Mask Region-Based convolutional neural network (Mask-RCNN) presents a unique pattern for glaucoma and non-glaucoma affected eyes, which work may utilize to detect glaucoma using machine learning techniques. In this study, the usage of Mask-RCNN is used to create a hierarchical structure for differentiating between images of glaucoma affected and non-affected eyes, allowing for a more accurate categorization of glaucoma patients. With our proposed method may evaluate at six different degrees of complexity and 33 convolutional layers. When detecting glaucoma disease, the proposed research uses the dropout mechanism to enhance the overall efficacy of the performance, as described above. the SCES and ORIGA datasets were used in this research to analyze the intended work, and the results were presented in this paper. the Accuracy of RCNN on the ORIGA dataset was 92.32 percent, Faster-RCNN was 93.89 percent, and Mask RCNN was 95.72 percent, according to the values obtained. When applied to the SCES dataset, the results showed that the Accuracy of the RCNN was 91.56 percent, that of the Faster-RCNN was 94.32 percent, and that of Mask RCNN was 97.56 percent. the mAP of RCNN was 89 percent, Faster-RCNN was 92 percent, and Mask RCNN was 90 percent. mAP of RCNN is 90 percent, Faster-RCNN is 91 percent, and Mask RCNN is 93 percent, according to the results.

关键词： Deep learning Visualization Medical treatment Aerospace electronics complexity theory Convolutional neural networks Reliability

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Intra-frame fast coding algorithm for 3D-HEVC depth map based on two-dimensional entropy and variance

Intra-frame fast coding algorithm for 3D-HEVC depth map base...

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IEEE Advanced Information Technology, Electronic and Automation Control conference (IAEAC)

作者： Wangze Yao Hongfei Huang ZhengXuan Wu Hua Zhang School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China

ISBN: (数字)9781728180281

ISBN: (纸本)9781728180298

the 3D-High Efficiency Video Coding Standard (HEVC)[1] improves the coding quality by employing multi-viewpoint and depth map coding formats. However, the 3D-HEVC algorithm has high time complexity due to the determination process of the coding unit (CU) size and the recursive rate-distortion cost calculation. To address this problem, we present a fast algorithm, which aims to reduce the time of not only deep image segmentation decision but also the powerful RD optimization. through fast decision-making on the 3D-HEVC depth map coding unit (CU), which is based on two-dimensional entropy of image and variance, the coding efficiency is improved rapidly. the benchmark experimental on the basic database show that the coding time of the proposed algorithm is reduced by 36.7%.

关键词： Video coding Image coding Encoding Entropy Partitioning algorithms Time complexity Standards

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the hermes mission: A cubesat constellation for multi-messenger astrophysics 5th

The hermes mission: A cubesat constellation for multi-messen...

引用

5th IAA conference on University Satellite Missions and Cubesat Workshop, 2020

作者： Scala, Francesca Zanotti, Giovanni Curzel, Serena Fetescu, Mirela Lunghi, Paolo Lavagna, Michèle Bertacin, Roberto Politecnico di Milano Department of Aerospace Science and Technology Via La Masa 34 Milano20156 Italy Politecnico di Milano Dipartimento di Elettronica Informazione e Bioingegneria Via Ponzio 34/5 Milano20133 Italy Via del Politecnico snc Roma00133 Italy

ISBN: (纸本)9780877036715

the High Energy Rapid Modular Ensemble of Satellites (HERMES) is a project that plays a key role in the Multi-Messenger Astrophysics of the next decade. the implementation of a distributed space asset to continuously monitor the random electromagnetic emissions of bright high-energy transients in the Universe is proposed. Specifically, the technological and scientific pathfinder constellation, up to six 3U CubeSat, embarks a new generation of miniaturised detectors for science. Having just closed the PDR, the project goes into phase C&D: Politecnico di Milano, together with INAF, oversees the space segments and payload implementation, respectively. the challenging mission feasibility was assessed from both technological and scientific points of view. the possibility to correctly keep the spacecraft triplets geometry and to ensure the correct overlapping of instrument field-of-view for a successful triangulation of highenergy transient has been achieved with no orbital control on board. the paper, starting from an introduction on scientific mission constraints, focuses on the description of platform subsystems design. the constellation complexity is highlighted in terms of pointing accuracy, power demand, and scientific data packages transmission. the program is co-funded by the National Ministry for Research (MIUR), the italian Space Agency, (HERMES-TP), and the EC-H2020 framework (HERMES-SP). © 2020, Univelt Inc., All rights reserved.

关键词： Astrophysics

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A Comparative Study of Various Pitch Detection algorithms

A Comparative Study of Various Pitch Detection Algorithms

引用

IEEE International conference on Computing, Communication and Security (ICCCS)

作者： Prajwal S Rao S Khoushikh Sriram Ravishankar R Advaith Ananthkrishnan K Balachandra B.M.S. College of Engineering Bengaluru India

ISBN: (数字)9781728191805

ISBN: (纸本)9781728191812

Digital Signal Processing deals with performing varied mathematical operations on different types of signals and in this paper, we study pitch estimation algorithms applied on music signals. We give a detailed study of time domain and frequency domain pitch detection algorithms namely Modified Autocorrelation, Average Magnitude Difference function, Yin, Cepstrum. the algorithms have been tested with real and synthesized music signals and the performances were compared based on time complexity and error obtained.

关键词： Signal processing algorithms Music Multiple signal classification Security Detection algorithms Time-domain analysis Time complexity

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Advanced Isomap Based on Data Nuggets Algorithm 5

Advanced Isomap Based on Data Nuggets Algorithm

引用

5th IEEE International conference on Computer and Communications, ICCC 2019

作者： Liu, Yating Rutgers University Department of Statistics Piscataway United States

ISBN: (纸本)9781728147437

Manifold learning is a recent popular approach to find the internal low-dimensional structure from the high-dimensional nonlinear data sets embedded in the European space. It is based on the idea that the data we can observe are actually mapped from a low-dimensional space to a high-dimensional one. Given the limitation of the internal characteristics of the data, those artificial high-dimensional data produce dimensional redundancy. In fact, these data can be represented in the lower-dimension space. algorithms for this task are to achieve efficient and accurate data analysis results. After studying some common algorithms purposed for manifold learning, we find that there is still an improvement for their efficiency. To be specific, in order to reduce computational complexity of the Isometrical Mapping, we introduce a new solution through the concept of data nuggets' to do data partition and selection. this concept is to reduce a large dataset into a smaller set of nuggets of data, each of which contains a center, weight and a scale parameter. While the data is re-expressed as data nuggets, we can apply algorithms for Isomap. this combination can largely reduce the computational complexity due to the decreased dataset. Meanwhile, it still maintains the advantages of Isomap that keep the geodesic distances between the points unchanged. © 2019 IEEE.

关键词： Computational complexity

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Universal Nonlocal Fuzzy C-means for Image Segmentation

Universal Nonlocal Fuzzy C-means for Image Segmentation

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International conference on Mechanical, Control and Computer Engineering (ICMCCE)

作者： Tingting Liu Hongyan Han Zhonggui Sun School of Mathematical Sciences Liaocheng University Liaocheng China

As an active tool for image segmentation, the classical fuzzy c-means (FCM) clustering algorithm is sensitive to noises. To overcome the weakness, several improved versions of FCM have been developed. Compared with the original one, these methods are more robust to noises. However, they are still not suitable for complex noises. To get better image segmentation results, especially in complex noisy scenarios, we provide a universal nonlocal FCM algorithm (UNFCM) by regularizing the objection function with a universal nonlocal spatial term. the experimental results on synthetic and real images suggest that the proposed UNFCM can yield higher quality segmentation results both in classification accuracy and visual effects.

关键词： Image segmentation Clustering algorithms Tools Visual effects Classification algorithms Noise measurement Computational complexity

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