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Article icaps-dfake: An integrated capsule-based model for deepfake image and video detection

Future Internet 2021年第4期13卷

作者： Khalil, Samar Samir Youssef, Sherin M. Saleh, Sherine Nagy Computer Engineering Department College of Engineering and Technology Arab Academy for Science Technology and Maritime Transport Alexandria1029 Egypt

Fake media is spreading like wildfire all over the internet as a result of the great advance-ment in deepfake creation tools and the huge interest researchers and corporations are showing to explore its limits. Now anyone can create manipulated unethical media forensics, defame, humiliate others or even scam them out of their money with a click of a button. In this research a new deepfake detection approach, iCaps-Dfake, is proposed that competes with state-of-the-art techniques of deep-fake video detection and addresses their low generalization problem. Two feature extraction methods are combined, texture-based Local Binary Patterns (LBP) and Convolutional Neural Networks (CNN) based modified High-Resolution Network (HRNet), along with an application of capsule neural networks (CapsNets) implementing a concurrent routing technique. Experiments have been con-ducted on large benchmark datasets to evaluate the performance of the proposed model. Several performance metrics are applied and experimental results are analyzed. The proposed model was primarily trained and tested on the DeepFakeDetectionChallenge-Preview (DFDC-P) dataset then tested on Celeb-DF to examine its generalization capability. Experiments achieved an Area-Under Curve (AUC) score improvement of 20.25% over state-of-the-art models. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： Local binary pattern

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Detection of Physical Anomalies in IoT Networks Using Machine Learning Techniques

Detection of Physical Anomalies in IoT Networks Using Machin...

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Advancement in Renewable Energy and Intelligent Systems (AREIS), International Conference on

作者： M. A. Mukunthan Jose Anand A. Stalin Kesavan Malini Mutyala R. Kesavan R. Geetha Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of ECE KCG College of Technology Chennai Tamil Nadu India Department of Marine Engineering Academy of Maritime Education and Training Chennai Tamil Nadu Department of CSBS Vignana Bharathi Institute of Technology Hyderabad Telangana India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamilnadu Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamilnadu

ISBN: (数字)9798350387230

ISBN: (纸本)9798350387247

This work focuses on the implementation of a Machine Learning (ML) algorithm for detecting physical anomalies in Internet of Things (IoT) networks. A Wireless Sensor Network (WSN) was deployed, collecting data under two scenarios: normal operation and an altered state exhibiting physical anomalies. The WSN, composed of humidity, Hall effect, barometer, and temperature sensors, communicated using the MQTT protocol with an Edge node for data capture. Data profiling and preprocessing were conducted to clean and optimize the dataset for ML. The K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF) algorithm were employed to classify network traffic, with hyperparameters tuned using Randomized Search CV. The dataset was split into training (75%) and testing (25%) sets, with performance metrics such as accuracy, precision, recall, F1 score, and ROC curve analyzed. The final model achieved high accuracy (99.53 %) and showed excellent precision (1) with a classification time of 0.0012 seconds per packet. These results demonstrate the RF model's effectiveness in detecting physical anomalies in IoT networks, although further optimization may be needed for real-world scalability.

关键词： Support vector machines Radio frequency Performance evaluation Wireless sensor networks Accuracy Computational modeling Scalability Nearest neighbor methods Reliability Internet of Things

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Selective ensemble learning for cross-muscle ALS disease identification with EMG signal

Selective ensemble learning for cross-muscle ALS disease ide...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xujian Wang Shenghua Teng Chenxu Hao Yan Liu Ji He Shuo Zhang Dongsheng Fan College of Electronic and Information Engineering Shandong University of Science and Technology China School of Computer Science and Technology University of Chinese Academy of Sciences China Department of Neurology Peking University Third Hospital China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Electromyography (EMG) provides a useful way to identify amyotrophic lateral sclerosis(ALS) disease. EMG signals sampled from different muscles show different sensitivities on ALS disease identification, in which to find out the most sensitive muscles to ALS is meaningful. In this paper, a selective ensemble learning method is proposed for cross-muscle ALS disease identification. First, omics features are extracted from time, frequency and wavelet domains of the original EMG signals and their adaptively decomposed components respectively. Second, the ensemble learning method with selective voting strategy is proposed for ALS identification in cross-individual and cross-muscle scenarios. Finally, the contributions of each sample to the individual identification are comprehensively analyzed using the ridge regression model. Two EMG datasets from different human race and different devices are used to evaluate the performance of the proposed method. Experimental results illustrate the effectiveness of the proposed method on cross-individual and cross-muscle ALS identification, i.e. the classification accuracy and sensitivity improve by 1% ~ 11% and 1%. ~ 18% respectively.

关键词： Time-frequency analysis Wavelet domain Sensitivity Muscles Feature extraction Electromyography Ensemble learning

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MTSC-GE: A Novel Graph based Method for Multivariate Time Series Clustering 12

MTSC-GE: A Novel Graph based Method for Multivariate Time Se...

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12th IEEE International Conference on Big Knowledge, ICBK 2021

作者： Yang, Ze Tai, Changyang Wu, Gongqing Zhang, Zan Bao, Xianyu Key Laboratory of Knowledge Engineering With Big Data of Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

ISBN: (纸本)9781665438582

Few clustering methods show good performance on multivariate time series (MTS) data. Traditional methods rely too much on similarity measures and perform poorly on the MTS data with complex structures. This paper proposes an MTS clustering algorithm based on graph embedding called MTSC-GE to improve the performance of MTS clustering. MTSC-GE can map MTS samples to the feature representations in a low-dimensional space and then cluster them. While mining the information of the samples themselves, MTSC-GE builds the whole time series data into a graph, paying attention to the connections between samples from an overall perspective and discovering the local structural feature of MTS data. The proposed MTSC-G E consists of three stages. The first stage builds a graph using the original dataset, where each of the MTS samples is regarded as a node in the graph. The second stage uses the graph embedding technique to obtain a new representation of each node. Finally, MTSC-G E uses the K - Means algorithm to cluster based on the newly obtained representation. We compare MTSC-GE with six state-of-the-art benchmark methods on five public datasets, experimental results show that MTSC-GE has achieved good performance. © 2021 IEEE.

关键词： Data mining

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Edge Amplification in Cellphone and Gaze Unlicensed Bands

Edge Amplification in Cellphone and Gaze Unlicensed Bands

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Global Conference for Advancement in technology (GCAT)

作者： M.S.Kavitha Nitish Vashisht Girish Kalele M.Vigenesh S.Kannimuthu Shilpa M. Katikar Electronics and Communication Engineering R.M.K.Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Department of Information Technology Karpagam College of Engineering Coimbatore Computer Vishwakarma Institute of Technology Pune

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

There is significant possibility to use bandwidth (in the 28 - 38 GHz bands) and indeed the 60 GHz band for mitochondrial and classmates’ wireless technologies as the price of enormously WiFi processors at micrometer wave (nanometers) rates continues to fall. Using thruster cell towers and a wireless service rolling based on the measured path particular input at bandwidth of 38 GHz and 60 GHz, this research provides dimensions of urban cytoskeleton and participant multi - band tv stations. The transmit delay distributed and insertion loss as a role of divorce maximum range and base station referring directions for a variety of real-life settings. According to the statistics provided here, carriers have significant dynamic routing delay distributions and can take advantage of a wide range of aiming angles to just provide dispersal connections at 38 GHz, where unimpeded lines of follow free space antenna radiation loss. At 60 GHz, there is noticeably more route loss, less variation in lag patterns, and fewer special station orientations for establishing links. We segments known correlations between RMS interruption spread and aerial aiming angles in both the 38 GHz and 60 GHz bands. We also note that extra route loss (above empty space) has a negative correlation with transponder spacing.

关键词： Wireless communication TV Program processors Poles and towers Millimeter wave measurements Bandwidth Receivers Propagation losses Delays Wireless fidelity

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A GAN-Based Data Poisoning Attack Against Federated Learning Systems and Its Countermeasure

arXiv

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arXiv 2024年

作者： Sun, Wei Gao, Bo Xiong, Ke Wang, Yuwei The Engineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education School of Computer Science and Technology The Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing100044 China The Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

As a distributed machine learning paradigm, federated learning (FL) is collaboratively carried out on privately owned datasets but without direct data access. Although the original intention is to allay data privacy concerns, "available but not visible" data in FL potentially brings new security threats, particularly poisoning attacks that target such "not visible" local data. Initial attempts have been made to conduct data poisoning attacks against FL systems, but cannot be fully successful due to their high chance of causing statistical anomalies. To unleash the potential for truly "invisible" attacks and build a more deterrent threat model, in this paper, a new data poisoning attack model named VagueGAN is proposed, which can generate seemingly legitimate but noisy poisoned data by untraditionally taking advantage of generative adversarial network (GAN) variants. Capable of manipulating the quality of poisoned data on demand, VagueGAN enables to trade-off attack effectiveness and stealthiness. Furthermore, a cost-effective countermeasure named Model Consistency-Based Defense (MCD) is proposed to identify GAN-poisoned data or models after finding out the consistency of GAN outputs. Extensive experiments on multiple datasets indicate that our attack method is generally much more stealthy as well as more effective in degrading FL performance with low complexity. Our defense method is also shown to be more competent in identifying GAN-poisoned data or models. The source codes are publicly available at https://***/SSssWEIssSS/VagueGAN-Data-Poisoning-Attack-and-Its-Countermeasure. Copyright © 2024, The Authors. All rights reserved.

关键词： Cost effectiveness

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Incentive-Driven Federated Learning for Collaborative Agricultural Consumer Electronics

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IEEE Transactions on Consumer Electronics 2025年

作者： Zheng, Xiao Tahir, Muhammad Anwar, Muhammad Shahid Tang, Yongwei Ahmad, Sadique ShanDong University of Technology School of Computer Science and Technology Zibo 255000 China Defense View Faculty of Engineering Science and Technology(FEST) Iqra University Departmentof Computer Science Main Campus Defense View Sindh Karachi 75500 Pakistan Department of Ai and Software Gachon University Seongnam-si 13120 South Korea Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education China Prince Sultan University EIAS: Data Science and Blockchain Laboratory College of Computer and Information Sciences Riyadh 11586 Saudi Arabia

To address the challenges of data privacy protection and collaborative management in agricultural consumer electronics (ACE) devices, this study proposes a distributed data processing framework based on federated learning (FL). The framework employs a three-tier system architecture comprising a base station (BS), edge devices that train models using local data, and a BS that aggregates these models to generate a global one. This iterative process establishes a paradigm that balances privacy preservation and performance optimization. Additionally, we design an incentive mechanism based on auction theory to simulate the buyer-seller interaction between BSs and edge devices. In this mechanism, devices submit bids according to their minimum energy requirements. To maximize resource allocation utility, we propose a greedy auction algorithm that satisfies multiple economic properties. Simulation results demonstrate that the algorithm not only guarantees these properties but also enhances system utility and efficiency. © 2025 IEEE.

关键词： Agricultural Consumer Electronic Auction Game Federated Learning (FL) Incentive Mechanisms Resource Optimisation

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Applying data mining technique to predict trends in air pollution in Mumbai 1

Applying data mining technique to predict trends in air poll...

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1st International Conference on Advances in Computational science and engineering, ICACSE 2020

作者： Sadhasivam, Jayakumar Muthukumaran, V. Thimmia Raja, J. Vinothkumar, V. Deepa, R. Nivedita, V. Vellore Institute of Technology Vellore India Department of Mathematics School of Applied Science REVA University Bengaluru64 India School of Computing Kalasalingam Academy of Research and Education Tamilnadu 626128 India Department of Computer Science and Engineering MVJ College of Engineering Bangalore India Telangana Hyderabad India Department of Computer Science and Engineering Dr. MGR Educational and Research Institute Chennai India

Prediction of air quality is a topic of great interest in air quality research due to direct association with health effect. The prediction provides pre-information to the overall population of the area about the status of pollution on which they can take precautionary measures and can protect their health. The problem arises when the level of SO2, NO2 and residual suspended particulate matters in the air increases than that of theirs restricted level. In this paper, the Prophet Algorithm, open source software, is applied to predict the trend of air pollution in the city of Mumbai, Maharashtra. The Prophet is machine learning algorithm to forecast and also to predict time series data. It is based on additive model where non-linear trends are fit with yearly and weekly seasonality. The graphical results are generated after using this algorithm which shows the trending pattern of the pollutants in the air of Mumbai. © Published under licence by IOP Publishing Ltd.

关键词： Air quality

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Enhanced Language Representation with Label Knowledge for Span Extraction

Enhanced Language Representation with Label Knowledge for Sp...

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2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021

作者： Yang, Pan Cong, Xin Sun, Zhenyun Liu, Xingwu Institute of Computing Technology Chinese Academy of Sciences China PCG Tencent Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China School of Mathematical Sciences Dalian University of Technology China

ISBN: (纸本)9781955917094

Span extraction, aiming to extract text spans (such as words or phrases) from plain texts, is a fundamental process in Information Extraction. Recent works introduce the label knowledge to enhance the text representation by formalizing the span extraction task into a question answering problem (QA Formalization), which achieves state-of-the-art performance. However, QA Formalization does not fully exploit the label knowledge and suffers from low efficiency in training/inference. To address those problems, we introduce a new paradigm to integrate label knowledge and further propose a novel model to explicitly and efficiently integrate label knowledge into text representations. Specifically, it encodes texts and label annotations independently and then integrates label knowledge into text representation with an elaborate-designed semantics fusion module. We conduct extensive experiments on three typical span extraction tasks: flat NER, nested NER, and event detection. The empirical results show that 1) our method achieves state-of-the-art performance on four benchmarks, and 2) reduces training time and inference time by 76% and 77% on average, respectively, compared with the QA Formalization paradigm. Our code and data are available at https://***/Akeepers/LEAR. © 2021 Association for Computational Linguistics

关键词： Benchmarking

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ER-ERT:A Method of Ensemble Representation Learning of Encrypted RAT Traffic

ER-ERT:A Method of Ensemble Representation Learning of Encry...

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IFIP Networking Conference

作者： Yijing Zhang Hui Xue Jianjun Lin Xiaoyu Liu Weilin Gai Xiaodu Yang Anqi Wang Yinliang Yue Bo Sun Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences Institute of Software Chinese Academy of Sciences School of computer science and technology University of Chinese Academy of Sciences Coordination Center of China National Computer Network Emergency Response Technical Team Liaoning University of Technology Zhongguancun Laboratory Beijing P.R.China

Remote Access Trojan (RAT) is one of the major threats to today's network environment. It is a class of malware frequently used by hacking collectives to monitor victims' actions and steal personal information in targeted computers. Traditional machine learning algorithms have been widely used to detect malicious encrypted RAT traffic. Traditional machine learning algorithms rely deeply on expert experience, and it is difficult for current traffic classification models to design effective handcraft features. Deep learning methods have been introduced in recent years to generate representations from raw network traffic data automatically. Previous deep learning-based malicious traffic detection methods generate representations from flow sequences or packet payload bytes. None of these methods simultaneously learn embeddings from flow sequence and packet payload bytes. Thus, we propose a novel ensemble model to draw fine-grained and multi-angle traffic representations for RAT traffic. The model extract (1) temporal features with convolution neural network (CNN) and the Reproducing Kernel Hilbert Space (RKHS) embedding method to model network flow sequence, (2) spatial features with autoencoder and bidirectional gated recurrent unit (Bi-GRU) network to model packet payload bytes, and (3) some stage-based attributes to enhance the identification ability of RAT traffic behaviors. According to the experimental result, our approach achieves better performance than previous works with a precision rate of 97.0% and a recall rate of 96.5%.

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