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Facial deepfake performance evaluation based on three detection tools: MTCNN, Dlib, and MediaPipe

AIP Conference Proceedings 2024年第1期3097卷

作者： Mohammed Thajeel Abdullah Nada Hussein M. Ali Informatics Institute for Postgraduate Studies Iraqi Commission for Computer and Informatics Baghdad Iraq Department of Computer Science College of Science University of Baghdad Baghdad Iraq

DeepFake, DeepFake detection, and face detection technologies have a strong relationship. DeepFake techniques have become a serious threat to celebrities, the general public, and the judiciary, which relies on visual media as evidence in criminal cases. DeepFake detection methods are responsible for detecting DeepFake through a series of procedures, the first and most significant of which is face detection. Note that face detection approaches are used in several methods to detect faces in photos, including those in DeepFake generating and DeepFake detection. Hence, the first part of this paper describes the concept of face detection and three of its tools, two of which are frequently utilized in DeepFake detection (Multi-Task Cascaded Convolutional Networks (MTCNN) and Dlib). MediaPipe has not yet been employed in this field to study and evaluate the performance of these tools through a practical comparison. Consequently, a group of modern DeepFake detection methods proposes a new taxonomy based on extracting the features utilized in each. Lastly, datasets of photos from DeepFake detection experiments were used to determine which one posed a genuine challenge to the three tools. The results presented that the MediaPipe tool is the best in accuracy, 99.3%, and the dataset Open Forensic (OF) is the most challenging due to the many mistakes generated when its works on it.

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Probabilistic Scheme for Intelligent Jammer Localization for Wireless Sensor Networks

Probabilistic Scheme for Intelligent Jammer Localization for...

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Intelligent Computing and Networking - Proceedings of IC-ICN 2023

作者： Yadav, Amar Pal Davuluri, Sandeep Kumar Charan, Piyush Keshta, Ismail Gavilán, Juan Carlos Orosco Dhiman, Gaurav CSE-AI College: Noida Institute of Engineering and Technology Greater Noida Greater Noida India Department of Information Technology University of the Cumberlands WilliamsburgKY40769 United States Electronics and Communication Engineering Manav Rachna University Faridabad India Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Universidad Privada del Norte campus virtual Lima Peru Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9789819931767

With the development of artificial intelligence technology, intelligent interference sources can improve the interference effect by changing their transmission power, which leads to the failure of traditional positioning technology based on received signal strength. Therefore, a sensor wake-up mechanism is introduced to study multi-interference sources based on block-compressed sensing positioning method. First, wake up the sensor nodes periodically, while improving the utilization effectiveness of the sensor nodes and the accuracy of positioning information collection;secondly, considering that the distance between the distance and the received signal strength cannot be determined when the transmission power of the interference source is unknown and changes. Then, based on the compressive sensing theory, the positioning problem is modeled as a block sparse vector reconstruction problem;finally, a variation-based algorithm is designed by exploring the law of power variation. The Wake-VBEM reconstruction algorithm based on Bayesian mean expectation accurately reconstructs the target position vector. The simulation proves that the proposed method can simultaneously realize the position estimation of multiple interference sources and effectively improve the service life of the network when the interference source power is unknown and changes. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Artificial intelligence

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Orthogonal Learning Rafflesia Optimization Algorithm with Quasi-Affine Transformation Evolutionary and Its Application in Wireless Sensor Networks

Orthogonal Learning Rafflesia Optimization Algorithm with Qu...

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Jeng-Shyang Pan Xin- Yi Zhang Shu-Chuan Chu Ning Zhong College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Department of Information Managemen Chaoyang University of Technology Taiwan Department of Life Science and Informatics Maebashi Institute of Technology Maebashi Japan 4 Faculty of Information Technology Beijing University of Technology Beijing China

The Rafflesia Optimization Algorithm (ROA) is an optimization algorithm that mimics the growth cycle of the Rafflesia. Building upon the ROA, this study introduces a novel heuristic algorithm called Orthogonal Learning Quasi-Affine Transformation Evolutionary Rafflesia Optimization Algorithm (OLQROA). The QUATRE method and the Orthogonal Learning approach are combined in the OLQROA algorithm. Compare OLQROA with ROA algorithm, improved ROA algorithms, and other three mature algorithms using CEC2017 benchmark function. The outcomes of the experiments show that OLQROA works better than the aforementioned algorithms. Additionally, OLQROA is used to apply three-dimensional wireless sensor coverage, producing superior results in comparison to the afore-mentioned algorithms.

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Interpretable Fake News Detection with Topic and Deep Variational Models

arXiv

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arXiv 2022年

作者： Hosseini, Marjan Sabet, Alireza Javadian He, Suining Aguiar, Derek Department of Computer Science and Engineering University of Connecticut StorrsCT06269 United States Department of Informatics and Networked Systems University of Pittsburgh PittsburghPA15260 United States

The growing societal dependence on social media and user generated content for news and information has increased the influence of unreliable sources and fake content, which muddles public discourse and lessens trust in the media. Validating the credibility of such information is a difficult task that is susceptible to confirmation bias, leading to the development of algorithmic techniques to distinguish between fake and real news. However, most existing methods are challenging to interpret, making it difficult to establish trust in predictions, and make assumptions that are unrealistic in many real-world scenarios, e.g., the availability of audiovisual features or provenance. In this work, we focus on fake news detection of textual content using interpretable features and methods. In particular, we have developed a deep probabilistic model that integrates a dense representation of textual news using a variational autoencoder and bi-directional Long Short-Term Memory (LSTM) networks with semantic topic-related features inferred from a Bayesian admixture model. Extensive experimental studies with 3 real-world datasets demonstrate that our model achieves comparable performance to state-of-the-art competing models while facilitating model interpretability from the learned topics. Finally, we have conducted model ablation studies to justify the effectiveness and accuracy of integrating neural embeddings and topic features both quantitatively by evaluating performance and qualitatively through separability in lower dimensional embeddings. © 2022, CC BY.

关键词： Long short-term memory

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A Fuzzy-Logic Based Path Loss Model at 3.4 GHz for LTE Networks

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Open Journal of Applied sciences 2022年第7期12卷 1271-1283页

作者： Stephen Ojo Taiwo P. Ojo Victor Ojong Etta College of Engineering Anderson University Anderson USA Department of Management Information Systems Girne American University Girne Northern Cyprus Computer Science Department Florida A & M University Tallahassee USA

Empirical and deterministic models have not proven to be effective in path loss predictions because of the problems of computational complexities, low accuracies, and inability to generalize. To solve these problems relating to path loss predictions, this article presents an optimal path loss propagation model developed at 3.4 GHz with the use of fuzzy logic. We introduced Fuzzy logic to accurately represent all forms of uncertainties in the data spectrum as the signal propagates from the transceiver to the receiver, thereby producing accurate results. Experimental data were collected across Cyprus at 3.4 GHz and compared with three existing path loss models. The fuzzy-logic path loss prediction model was then developed and compared with the experimental data and with each of the theoretical empirical models, the newly developed model predicted signal loss with the greatest accuracy as it gives the lowest root-mean-square error. The newly developed model is very efficient for signal propagation and path loss prediction.

关键词： Signal Loss Fuzzy-Logic Machine Learning Signal Propagation Accuracy Empirical Deterministic

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AI-driven IoT for smart health care: Security and privacy issues

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informatics in Medicine Unlocked 2022年 30卷

作者： Keshta, Ismail Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia

The Internet of Things (IoT) has recently brought the dream of a smarter world into an accurate picture with various services and a significant amount of data. With the innovation of smart Multiple Sensorial Media (MulSeMedia) systems, the cloud, and things technologies, “smart health care” is receiving considerable attention from health care communities, industry, government, and academia. The purpose of the present research has been to establish the AI-driven IoT (AIIoT) security and privacy issues and recommend the manner in which all such matters can be adequately addressed. The study employed a qualitative study design in which qualitative information was collected from existing relevant secondary sources. The evidence from the findings shows that the evolution of AI-driven IoT (AIIoT) has added to the significant number of new sensors and devices to the internet, creating an assortment of security and privacy concerns among the users. The study recommends that the current requirement for the AI-driven IoT (AIIoT) is to have certain types of well-defined architecture standards that include interfaces and data models that ensure that the privacy and security of the users are enhanced. © 2022

关键词： AI AIIoT Health care IoT Privacy, and security

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Pupil detection schemes in human eye: a review

Pupil detection schemes in human eye: a review

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作者： Min-Allah, Nasro Jan, Farmanullah Alrashed, Saleh Department of Computer Science College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Management Information Systems Department College of Applied Studies and Community Service Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia

Pupil detection in a human eyeimage or video plays a key role in many applications such as eye-tracking, diabetic retinopathy screening, smart homes, iris recognition, etc. Literature reveals pupil detection faces many complications including light reflections, cataract disease, pupil constriction/dilation moments, contact lenses, eyebrows, eyelashes, hair strips, and closed eye. To cope with these challenges, research community has been struggling to devise resilient pupil localization schemes for the image/video data collected using the near-infrared (NIR) or visible spectrum (VS) illumination. This study presents a critical review of numerous pupil detection schemes taken from standard sources. This review includes pupil localization schemes based on machine learning, histogram/thresholding, Integro-differential operator (IDO), Hough transform and among others. The probable pros and cons of each scheme are highlighted. Finally, this study offers recommendations for designing a robust pupil detection system. As scope of pupil detection is very broader, therefore this review would be a great source of information for the relevant research community. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Automation

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A Non-Iterative LMI-Based Design of Static Output Feedback Control with $\mathcal{L}_{2}$ Gain Performance for Linear systems

A Non-Iterative LMI-Based Design of Static Output Feedback C...

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International Conference on Electrical, Control and Instrumentation Engineering (ICECIE)

作者： Hidetoshi Oya Daiki Asada Shunya Nagai Kazushi Nakano Department of Computer Science Tokyo City University Tokyo Japan Graduate School of Integrative Science and Engineering Tokyo City University Tokyo Japan Department of Electronics and Control Engineering National Institute of Technology (KOSEN) Niihama College Ehime Japan Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo Japan

ISBN: (数字)9798350380040

ISBN: (纸本)9798350380057

In this paper, we present a new LMI-based non-iterative design strategy for static output feedback controllers with $\mathcal{L}_{2}$ Gain Performance for linear systems. In our approach, on the basis of the well-known necessary and sufficient condition for the existence of the static output feedback control for linear systems, the non-iterative LMI-based design procedure for the proposed static output feedback controller is derived. In this paper, we show the LMI-based non-iterative design procedure for static output feedback controllers with $\mathcal{L}_{2}$ Gain Performance and the effectiveness of the proposed static output feedback control is presented through a simple illustrative example.

关键词： Linear systems Sufficient conditions Instruments Interconnected systems Control systems Output feedback Gain

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Creating Own AI Datasets from Different Language Sources Efficiently: Advantages, Disadvantages and Best Pracitces to Make Own Datasets 6

Creating Own AI Datasets from Different Language Sources Eff...

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6th IEEE International Conference and Workshop Obuda on Electrical and Power Engineering, CANDO-EPE 2023

作者： Molnar, Gyorgy Cserko, Jazsef Nagy, Eniko Balogh, Zoltan Óbuda University Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary Széchenyi University János Csere Apáczai Faculty Győr Hungary John von Neumann University Gamf Faculty of Engineering and Computer Science Kecskemét Hungary Győr Hungary Institute of Cyberphysical Systems Óbuda University Antal Bejczy Center for Intelligent Robotics John von Neumann Faculty Budapest Hungary Constantine the Philosopher University in Nitra Faculty of Natural Sciences and Informatics Department of Informatics Nitra94901 Slovakia

ISBN: (纸本)9798350328752

Artificial Intelligence (AI) has become a ubiquitous technology that has the potential to revolutionize many industries. However, AI requires access to vast amounts of data to achieve its full potential. By understanding these data sources and the best practices for working with them, we can unlock the full potential of AI and create groundbreaking solutions that benefit society as a whole. Data sources can be utilized to train AI algorithms, including structured and unstructured data, but finding relevant datasets is challenging. The main problem is the international sources. Most of the datasets are available only in English or other common languages. However these are good sources, but translating them is expensive and time-consuming. This paper shows how to generate own datasets from open-source data with translation. © 2023 IEEE.

关键词： Artificial intelligence

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HEART: Achieving Timely Multi-Model Training for Vehicle-Edge-Cloud-Integrated Hierarchical Federated Learning

arXiv

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arXiv 2025年

作者： Yang, Xiaohong Liwang, Minghui Wang, Xianbin Cheng, Zhipeng Hosseinalipour, Seyyedali Dai, Huaiyu Jiao, Zhenzhen School of Informatics Xiamen University Fujian China Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems Japan Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai China Department of Electrical and Computer Engineering Western University ON Canada School of Future Science and Engineering Soochow University Suzhou China Department of Electrical Engineering University at Buffalo-SUNY BuffaloNY United States Department of Electrical and Computer Engineering NC State University RaleighNC United States iF-Labs Beijing Teleinfo Technology Company Ltd. CAICT Beijing China

The rapid growth of AI-enabled Internet of Vehicles (IoV) calls for efficient machine learning (ML) solutions that can handle high vehicular mobility and decentralized data. This has motivated the emergence of Hierarchical Federated Learning over vehicle-edge-cloud architectures (VEC-HFL). Nevertheless, one aspect which is underexplored in the literature on VEC-HFL is that vehicles often need to execute multiple ML tasks simultaneously, where this multi-model training environment introduces crucial challenges. First, improper aggregation rules can lead to model obsolescence and prolonged training times. Second, vehicular mobility may result in inefficient data utilization by preventing the vehicles from returning their models to the network edge. Third, achieving a balanced resource allocation across diverse tasks becomes of paramount importance as it majorly affects the effectiveness of collaborative training. We take one of the first steps towards addressing these challenges via proposing a framework for multi-model training in dynamic VEC-HFL with the goal of minimizing global training latency while ensuring balanced training across various tasks—a problem that turns out to be NP-hard. To facilitate timely model training, we introduce a hybrid synchronous-asynchronous aggregation rule. Building on this, we present a novel method called Hybrid Evolutionary And gReedy allocaTion (HEART). The framework operates in two stages: first, it achieves balanced task scheduling through a hybrid heuristic approach that combines improved Particle Swarm Optimization (PSO) and Genetic Algorithms (GA);second, it employs a low-complexity greedy algorithm to determine the training priority of assigned tasks on vehicles. Experiments on real-world datasets demonstrate the superiority of HEART over existing methods. © 2025, CC BY-NC-ND.

关键词： Federated learning

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