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Deepfake detection using deep learning methods: A systematic and comprehensive review

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2024年第2期14卷 e1520-e1520页

作者： Heidari, Arash Jafari Navimipour, Nima Dag, Hasan Unal, Mehmet Department of Software Engineering Haliç University Istanbul Turkey Department of Computer Engineering Kadir Has Universitesi Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan Management Information Systems Kadir Has Universitesi Istanbul Turkey Department of Computer Engineering Nisantasi Universitesi Istanbul Turkey

Deep Learning (DL) has been effectively utilized in various complicated challenges in healthcare, industry, and academia for various purposes, including thyroid diagnosis, lung nodule recognition, computer vision, large data analytics, and human-level control. Nevertheless, developments in digital technology have been used to produce software that poses a threat to democracy, national security, and confidentiality. Deepfake is one of those DL-powered apps that has lately surfaced. So, deepfake systems can create fake images primarily by replacement of scenes or images, movies, and sounds that humans cannot tell apart from real ones. Various technologies have brought the capacity to change a synthetic speech, image, or video to our fingers. Furthermore, video and image frauds are now so convincing that it is hard to distinguish between false and authentic content with the naked eye. It might result in various issues and ranging from deceiving public opinion to using doctored evidence in a court. For such considerations, it is critical to have technologies that can assist us in discerning reality. This study gives a complete assessment of the literature on deepfake detection strategies using DL-based algorithms. We categorize deepfake detection methods in this work based on their applications, which include video detection, image detection, audio detection, and hybrid multimedia detection. The objective of this paper is to give the reader a better knowledge of (1) how deepfakes are generated and identified, (2) the latest developments and breakthroughs in this realm, (3) weaknesses of existing security methods, and (4) areas requiring more investigation and consideration. The results suggest that the Conventional Neural Networks (CNN) methodology is the most often employed DL method in publications. According to research, the majority of the articles are on the subject of video deepfake detection. The majority of the articles focused on enhancing only one parameter, w

关键词： National security

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A comparative review for question answering frameworks on the linked data

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Recent Advances in computer Science and Communications 2021年第6期14卷 1695-1705页

作者： Tasar, Ceren O. Komesli, Murat Unalir, Murat O. Department of Software Engineering Faculty of Engineering Yaşar University Izmir35100 Turkey Department of Management Information Systems School of Applied Sciences Yaşar University Izmir35100 Turkey Department of Computer Engineering Faculty of Engineering Ege University Izmir35100 Turkey

Background: One of the state-of-the-art techniques for question answering frameworks is using linked data by converting the user input into SPARQL which is the query language for linked data. Objective: The main target is to emphasize the most fundamental issues while developing a question answering frameworks that accept input in natural language and converting it into SPARQL. Methods: The trend of applying linked data as a data source is gaining popularity among the re-searchers. In this study, question answering frameworks that combine both natural language processing techniques and linked data technologies are examined. Common principles of examined question answering frameworks recognize user intention, enriching natural language input and converting it to a SPARQL query. Results: 9 studies are selected for further examination to be compared by using selection criteria de-fined in the research methodology. Conclusion: Resulting outcomes are represented and compared in detail. In addition to the comparative review of systems, a general architecture of question answering frameworks on the linked data is drawn as an outcome of this study to provide a guideline for the researchers who are studying related research fields. © 2021 Bentham Science Publishers.

关键词： Linked data

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Product Recommendation System using Deep Learning based Recurrent Neural Network

Product Recommendation System using Deep Learning based Recu...

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International Conference on computer Communication and Informatics (ICCCI)

作者： S. Shanthi K. Nirmaladevi N. Mathiarasi As. Krithika Tharani A. Monisha R. Kuppuchamy N. Krishnamoorthy Department of Computer Science & Engineering Kongu Engineering College Perundurai India Department of Computer Applications PSNA College of Engineering and Technology Dindigul Tamilnadu India Department of Software Systems and Engineering Vellore Campus Vellore Institute of Technology India

Today’s marketing strategies place a high priority on comprehending customer sentiments. It will not only give businesses a better understanding of how their clients view their goods and/or services, but it will also give them suggestions on how to enhance their offerings. We make an effort to comprehend the relationship between various factors in customer reviews on an online store for women’s clothing. The proposed system also classifies each review, according to, whether it recommends the product in question or not, and whether it has a positive, negative, or neutral sentiment. With the exception of review titles and review texts, we used univariate and multivariate analyses on datasets features to accomplish these goals. Additionally, we have implemented a bidirectional recurrent neural network (RNN) with a long-short term memory unit (LSTM) for sentiment and recommendation.

关键词： Deep learning Recurrent neural networks Filtering Clothing Informatics Recommender systems Business

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Enhancing Human Posture Detection with Hybrid Deep VGG16 and Attention Mechanism Network

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Procedia computer Science 2025年 258卷 1209-1218页

作者： Ogundokun Roseline Oluwaseun Rytis Maskeliūnas Federick Oscar Oluwakayode A. Oki Department of Software Engineering Kaunas University of Technology Kaunas 44249 Lithuania Department of Computer Science Landmark University Omu-Aran Nigeria Department of Computer Systems Engineering Tshwane University of Technology (TUT) Pretoria South Africa Adioo Technology Abuja Nigeria Department of Information Technology East London Walter Sisulu University South Africa

Human Posture Detection (HPD) and classification are the initial steps toward various computer vision applications related to security, advertisement, and healthcare. Even though more than three decades have passed, most methods have been focused only on detecting standing people. However, in real applications, human postures may be significantly different, such as standing, sitting, lying, and crouching, and the shapes of a human are varied with viewpoints, thus making detection and classification difficult. Moreover, the gradual transition from one posture to another further complicates the determination of the number of postures to be classified. This paper uses a pre-trained model to determine the number of unique human postures in activities. We approached PC as a multiclass detection problem using the very deep VGG 16 Convolutional Neural Network (CNN) empowered by an attention mechanism. Extract frames from video data recorded, followed by the derivation of features through a convolutional neural network. Classify different postures—bending, exercise, lying, sitting, and standing—with VGG 16 and attention mechanism. It is trained on the ETRI-Activity3D-LivingLab Dataset. Our proposed (VEACT-CNN) framework has shown stability in HPD with an overall accuracy of 95% and an F-measure of 98% while generating a very low level of false alarms rate (FAR) of 0.02 (2%). For comparison purposes, the accuracy obtained by other models on the same dataset is VGG 16.

关键词： Vision-based human detection Posture classification VGG 16 Deep learning Pre-trained model Human posture detection

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Analysis of Legged Wheels with Spring Suspension for Stair-Climbing Robotics

Analysis of Legged Wheels with Spring Suspension for Stair-C...

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2025 International Russian Smart Industry Conference, SmartIndustryCon 2025

作者： Magomedov, Islam Aygumov, Timur Ordonez-Avila, Jose Luis Kadyrov Chechen state university Grozny Russia Dagestan State Technical University Department at of Software Engineering Computer Technology and Automated Systems Dagestan Russia San Pedro Sula Honduras

ISBN: (纸本)9798331511241

The following article looked into legged wheels with spring suspension. The idea behind the work was to see how adding springs in various places and their stiffness would impact the robot's ability to navigate through stairs. Due to the unconventional appearance of legged wheels, they are not suitable for even terrain passage. Hence, the idea was to add springs to minimize shock occurrence each time a leg touches the even environment. It was also important to equip springs throughout the whole path. As the robot progresses through steps, a greater disturbance of the structure occurs. Two different ideas were tested. In the first scenario, springs were attached between the holder of the wheels and the main body. In the second scenario, springs were directly added to the legs. The extruded part of the leg was divided into two, and a spring was embedded between these two parts. Different values of spring stiffness were also examined. The results illustrated that the structure would go further by implementing springs in all four wheels or in the rear. However, the structure was tested only on an upward incline. With other setups, it might be different with the rearrangement of the springs. It was also noted that with higher stiffness of the springs, the structure became unstable. In the second scenario, results illustrated the slightest progress. For instance, when the front wheels' legs were modified, they showed better results. To understand the influence in more depth, further investigation of the problem must be conducted. © 2025 IEEE.

关键词： Industrial robots

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A Novel Method for Pneumothorax Diagnosis and Segmentation Using Deep Convolution Neural Network

A Novel Method for Pneumothorax Diagnosis and Segmentation U...

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International Conference on computer Communication and Informatics (ICCCI)

A pneumothorax is the accumulation of air in the pleural space between the chest and lungs. Air that is in the lungs during breathing can leak out of the lungs and become trapped between the chest and lungs in the thoracic cavity, causing chest pain, difficulty breathing, and ultimately the complete collapse of the taut lungs. pneumothorax. Therefore, early diagnosis and treatment should be started. Neural networks have proven effective in image classification tasks, and many researchers believe that training neural network models can detect radiologist-diagnosed chest abnormalities such as pneumonia and pneumothorax. is proving. Moreover, we utilize various techniques, including Stochastic Weight Averaging (SWA), aggressive data augmentation, and learning rate schedule. We use the chest x-ray dataset provided by the 2019 SIIM-ACR In this study, a ResNet-34 neural network is trained on a data set of chest x-rays containing over 10,675 images to create a model that predicts the presence of pneumothorax from chest *** suggested system receives a Dice Similarity Coefficient (DSC) of 0.8410

关键词： Training Image segmentation Schedules Pulmonary diseases Neural networks Lung Stochastic processes

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Smart Farmer Education System for Disease Identification, Severity Assessment and Infestation Management (Brinjal) 17

Smart Farmer Education System for Disease Identification, Se...

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17th IEEE International Conference on Industrial and Information systems, ICIIS 2023

作者： Bandara, Keshara Vidhanaarachchi, Samitha Nanayakkara, A.A.E. Kulasinghe, Kavindu Rajapakshe, Sanka Wickramasinghe, Asela Yapa, Kanishka Sri Lanka Institute of Information Technology Department of Information Technology New Kandy Road Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka University of Ruhuna Faculty of Agriculture Department of Soil Science Mapalana Kamburupitiya Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350323634

The agriculture industry is one of the most significant sources of foreign exchange and employment in the Sri Lankan market. Therefore, small crops play a crucial role in ensuring the food security of the population as they are integral components of Sri Lankan cuisine. However, industry experts have identified inefficient disease and pest management as a hindrance to production. This issue requires immediate attention and proper action, which can be effectively addressed through the utilization of ICT technologies. Consequently, this research paper proposes a smart system that aims to assist farmers and agriculture professionals by providing them with farmer education, disease and infestation management tools, as well as progression level calculations. The implementation of this system aims to uplift the agriculture industry in Sri Lanka. The developed 'AI-based mobile application system for Brinjal Diseases,' which is based on analyzing the eating patterns of leafhopper damage, presents an innovative approach to identify the most significant threats to Brinjal, specifically focusing on leafhopper damage. This proposed system effectively informs various stakeholders, including the Area Agricultural Research Officer, Seed Certification and Plant Protection Center (SCPPC), Agricultural Service Center, Office of the Register of Pesticides (ROP), Plant Protection Service (PPS), as well as farmers, regarding the dispersion and prevention of brinjal diseases and pest infections. © 2023 IEEE.

关键词： Crowdsourcing

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KRATT: QBF-Assisted Removal and Structural Analysis Attack Against Logic Locking

arXiv

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arXiv 2023年

作者： Aksoy, Levent Yasin, Muhammad Pagliarini, Samuel Department of Computer Systems Tallinn University of Technology Tallinn Estonia Department of Computer and Software Engineering National University of Sciences and Technology Islamabad Pakistan Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh United States

This paper introduces KRATT, a removal and structural analysis attack against state-of-the-art logic locking techniques, such as single and double flip locking techniques (SFLTs and DFLTs). KRATT utilizes powerful quantified Boolean formulas (QBFs), which have not found widespread use in hardware security, to find the secret key of SFLTs for the first time. It can handle locked circuits under both oracle-less (OL) and oracle-guided (OG) threat models. It modifies the locked circuit and uses a prominent OL attack to make a strong guess under the OL threat model. It uses a structural analysis technique to identify promising protected input patterns and explores them using the oracle under the OG model. Experimental results on ISCAS’85, ITC’99, and HeLLO: CTF’22 benchmarks show that KRATT can break SFLTs using a QBF formulation in less than a minute, can decipher a large number of key inputs of SFLTs and DFLTs with high accuracy under the OL threat model, and can easily find the secret key of DFLTs under the OG threat model. It is shown that KRATT outperforms publicly available OL and OG attacks in terms of solution quality and run-time. © 2023, CC BY.

关键词： Structural analysis

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Intelligent System to Maximize Customer Experience in Restaurants

Intelligent System to Maximize Customer Experience in Restau...

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Advancements in Computing (ICAC), International Conference on

作者： R.U.A Jayalal Y.E Nimeshika G.N.N Samarasekara G.R.C.I Wijerathna Narmada Gamage Poojani Gunathilake Department of Computer Systems Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

With technological improvements in the restaurant industry, competition is fierce, and a restaurant's success depends on providing excellent and luxury customer satisfaction. The food industry has had to continuously advance with technology in order to adequately satisfy the wants of people leading increasingly luxurious lives. While some restaurants still rely on manpower, the majority are eager to advance with technology in order to improve the standard of their offerings and enhance their customers' experiences. In order to optimize customer satisfaction and experience, this research project intends to investigate the potential of intelligence in the restaurant business. This research has explored intelligent systems such as digital self-service menus for ordering food, augmented reality (AR) visualization systems for customers, the observation of chefs in the kitchen, and the usage of robots to serve meals. This research paper analyzes the advantages of automating restaurant operations and how to do so to enhance customer satisfaction. Overall, this study has the potential to transform the restaurant industry and increase customer satisfaction.

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Emotion and Mentality Monitoring Assistant (EMMA)

Emotion and Mentality Monitoring Assistant (EMMA)

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International Conference on Communication and Electronics systems (ICCES)

作者： Ishini Kiridena Dayan Marasinghe Ruvini Karunarathne Kavindu Wijethunga Harinda Fernando Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

Human emotions, psychology, and social well-being are all parts of mental health. It has an impact on how people feel, think, and act. It aids in figuring out how individuals act under pressure, interact with each other, and arrive at decisions. Every period of life, from childhood and adolescence to maturity, is vital for mental health. There are several elements that affect mental health issues, such as experiences with trauma or abuse in life, work-life stress, relationship difficulties, etc. Help is available for many of the mental health issues that are identified within a person. The proposed solution is implementing a friendly chatbot that is capable of capturing human emotional states (happy, angry, sad, etc.) and mental health states (depression, anxiety, stress, etc.) using human computer interaction and health record management. The proposed solution has been developed by considering both regular users and mental health practitioners. The user can communicate with the proposed assistant using text or voice to identify their emotions and mental health based on keywords, particular characteristics of voice and facial recognition used during the conversation. Once the mental health and emotion statuses are analyzed the electronic health records will be readily available on the blockchain as blockchain tokens, this could be shared with the practitioners securely.

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