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Deepfake Detection Using Adversarial Neural Network

computer Modeling in engineering & sciences 2025年第5期143卷 1575-1594页

作者： Priyadharsini Selvaraj Senthil Kumar Jagatheesaperumal Karthiga Marimuthu Oviya Saravanan Bader Fahad Alkhamees Mohammad Mehedi Hassan Department of Computer Science and Engineering Mepco Schlenk Engineering CollegeSivakasi626005India Department of Electronics and Communication Engineering Mepco Schlenk Engineering CllegeSivakasi626005India Department of Infomation Systems Colege f Computer and Information SciencesKing Saud UniversityRiyadhIl45Saud Arabia

With expeditious advancements in AI-driven facial manipulation techniques,particularly deepfake technology,there is growing concern over its potential *** pose a significant threat to society,partic-ularly by infringing on individuals’*** significant endeavors to fabricate systems for identifying deepfake fabrications,existing methodologies often face hurdles in adjusting to innovative forgery techniques and demonstrate increased vulnerability to image and video clarity variations,thereby hindering their broad applicability to images and videos produced by unfamiliar *** this manuscript,we endorse resilient training tactics to amplify generalization *** adversarial training,models are trained using deliberately crafted samples to deceive classification systems,thereby significantly enhancing their generalization *** response to this challenge,we propose an innovative hybrid adversarial training framework integrating Virtual Adversarial Training(VAT)with Two-Generated Blurred Adversarial *** combined framework bolsters the model’s resilience in detecting deepfakes made using unfamiliar deep learning *** such adversarial training,models are prompted to acquire more versatile *** experimental studies,we demonstrate that our model achieves higher accuracy than existing models.

关键词： Deepfake generalization forgery detection pixel-wise Gaussian blurring virtual adversarial training

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Al-Biruni Based Optimization of Rainfall Forecasting in Ethiopia

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computer Systems science & engineering 2023年第6期45卷 2885-2899页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Fadwa Alrowais Mostafa Abotaleb Abdelhameed Ibrahim Doaa Sami Khafaga Department of Communications and Elecronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science F aculty of Computer and Infomation SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of System Progamming South Ual State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

Rainfall plays a significant role in managing the water level in the *** unpredictable amount of rainfall due to the climate change can cause either overflow or dry in the *** individuals,especially those in the agricultural sector,rely on rain *** rainfall is challenging because of the changing nature of the *** area of Jimma in southwest Oromia,Ethiopia is the subject of this research,which aims to develop a rainfall forecasting *** estimate Jimma's daily rainfall,we propose a novel approach based on optimizing the parameters of long short-term memory(LSTM)using Al-Biruni earth radius(BER)optimization algorithm for boosting the fore-casting accuracy.N ash-Sutcliffe model eficiency(NSE),mean square error(MSE),root MSE(RMSE),mean absolute error(MAE),and R2 were all used in the conducted experiments to assess the proposed approach,with final scores of(0.61),(430.81),(19.12),and(11.09),***,we compared the proposed model to current machine-learning regression models;such as non-optimized LSTM,bidirectional LSTM(BiLSTM),gated recurrent unit(GRU),and convolutional LSTM(ConvLSTM).It was found that the proposed approach achieved the lowest RMSE of(19.12).In addition,the experimental results show that the proposed model has R-with a value outperforming the other models,which confirms the superiority of the proposed *** the other hand,a statistical analysis is performed to measure the significance and stability of the proposed approach and the recorded results proved the expected perfomance.

关键词： Rainfall prediction long short-term memory Al-Biruni earth radius algorithm meta-heuristic optimization

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Detection of Toxic Content on Social Networking Platforms Using Fine Tuned ULMFiT Model

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Intelligent Automation & Soft Computing 2023年第1期35卷 15-30页

作者： Hafsa Naveed Abid Sohail Jasni Mohamad Zain Noman Saleem Rao Faizan Ali Shahid Anwar Department of Software Engineering Faculty of ScienceUniversity of LahorePakistan Department of Computer Science COMSATS University IslamabadLahore CampusPakistan Institute for Big Data Analyics and Artifcial Intelligence(IBDAAI) Kompleks Al-KhawarizmiUniversii Tekoogi MARA40450Shah AlamSelangorMalaysia TechnoGenics SMC PVT LTD LahorePakistan Department of Computer and Infomation Science Universiti Teknologi PETRONASBandar Ser IskandarTronohPerakMalaysia Depatment of Information Engineering Technology National Skils University IslamabadSector H-8/1Faiz AhmedFaiz RoadIslamabadPakistan

Question and answer websites such as Quora,Stack Overflow,Yahoo Answers and Answer Bag are used by *** users post questions on these websites to get the answers from domain specific *** websites are multilingual meaning they are available in many different *** problem for these types of websites is to handle meaningless and irrelevant *** this paper we have worked on the Quora insincere questions(questions which are based on false assumptions or questions which are trying to make a statement rather than seeking for helpful answers)dataset in order to identify user insincere questions,so that Quora can eliminate those questions from their platform and ultimately improve the communication among users over the ***,a research was carried out with recurrent neural network and pretrained glove word embeddings,that achieved the F1 score of *** proposed study has used a pre-trained ULMFiT *** model has outperformed the previous model with an F1 score of 0.91,which is much higher than the previous studies.

关键词： Machine learning text mining quora mining artificial intelligence natural language processing

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Micro Aerial Vehicle Transmission of Flight Dynamics Telemetry

Micro Aerial Vehicle Transmission of Flight Dynamics Telemet...

引用

2021 IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2021

作者： Lau, Edwin M. Chiu, Alan Yung-Hung Ye, Eric Yu-Kai Chen, Paul Wei-Zhi Hong-Heng, Jhang Jian Wu, Jackson You-Jie Chaoyang University of Technology Department of Aeronautical Engineering Taichung Taiwan Chaoyang University of Technology Department of Computer Science Infomation Engineering Taichung Taiwan

ISBN: (纸本)9781665437554

There has been a good aspiration and hence considerable efforts in the development of flapping-wing micro aerial vehicles to increase their applications and utilizations. Owing to their sizes, their ability to carry payloads is stringent, and they are subject to unsteady aerodynamics. Developments of their flight controls are thus required to address instability issues. On-board transmission of flight dynamics data is necessary for appropriate flight control development. This study constructs a communication system for the acquisition of real-time flight telemetry. This is done via an onboard 9 degrees-of-freedom inertial unit, a local area network transmitter, and a motor driver. It overcomes the issues of weight, the center of gravity, and other stringent configuration problems for an agility airborne vehicle that transmits flight dynamic telemetry. This development enables further practical applications for MAV. © 2021 IEEE.

关键词： Flight dynamics

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A Method for Soybean Germination Rate Detection Based on Image Processing 49

A Method for Soybean Germination Rate Detection Based on Ima...

引用

49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Han, Ru Shu, Lei Yin, Menghan Deng, Der-Jiunn Li, Kailiang Zhang, Jin Yi, Xuefang School of Artificial Intelligence Nanjing Agricultural University China Nanjing Agricultural University NAU-Lincoln Joint Research Center of Intelligent Engineering China School of Engineering University of Lincoln United Kingdom National Changhua University of Education Department of Computer Science and Information Engineering Taiwan China Telecom Hongxin Infomation Technology Company Nanjing China

ISBN: (纸本)9798350331820

As the birthplace of soybeans, China has become the world's largest importer and consumer of soybeans. The supply and demand imbalance of soybeans has become one of the significant food and oil security concerns in *** solve this problem, this paper aims to study a soybean germination rate detection method based on image processing. By replacing manual detection of soybean germination rate and measuring soybean root length with machine-based approaches, basic image processing techniques including grayscale conversion, denoising, binarization, dilation, and erosion are applied to preprocess the images of soybean germination. An improved object detection algorithm is used to extract phenotypic features during soybean germination. The main focus of the study is twofold: (1) Measuring the seed area and the minimum bounding rectangle area of the image regions to determine the germination of seeds by comparing their ratio, thus obtaining the germination rate;(2) Using skeleton extraction and embryo-root separation methods to measure the changes in seed root length during germination, thereby selecting seeds with high vitality and improving the quality of soybean seeds. © 2023 IEEE.

关键词： Germination

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Using Machine Learning Techniques for Cancer Classification

Using Machine Learning Techniques for Cancer Classification

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2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Reddy, Ch. Srinivasa Singh, Rajesh Bhavani, R. Dasgupta, Sanhita Singh, Yashpal Pratan Singh, Suryabhan Vigan's Institute of Information Technology Department of IT Duvvada India Uttaranchal University Uttaranchal Institute of Technology India Saveetha Institute of Medical and Technical Sciences Institute of Computer Science and Engineering Saveetha School of Engineering Chennai600124 India Bir Bikram Department of Computer Memorial College Tripura Agartala India Jain Deemed to Be University Department of Computer Science & Engineering Karnataka Bengaluru India Deen Dayal Upadhyaya Gorakhpur University Institute of Engineering and Technology Department of Infomation Technology Gorakhpur India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

Given the extremely high death and suffering rates caused by breast cancer, women are particularly at risk. Because of this, it is imperative that algorithms be developed to find breast disease's early warning signs. Decision trees, random forests, regression analysis, and Mri are four categorised algorithms that detect cancer, and the end results were contrasted. Machine learning is essential for predicting breast cancer's early signs. Three data augmentation different classifiers will be used in this research. when categorising algorithms Data with unbalanced classifications are a serious issue that calls for careful treatment and preprocessing. We'll train the various M.L. models using a large dataset of breast cancer patients. The optimal method for this issue is ultimately determined by comparing all the strategies in terms of efficiency and precision. Our research will demonstrate the efficacy of several methods for this category of breast cancer so that we can use the proper strategy. © 2022 IEEE.

关键词： Convolutional neural networks

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LEARNING TO LEARN BY ZEROTH-ORDER ORACLE 8

LEARNING TO LEARN BY ZEROTH-ORDER ORACLE

引用

8th International Conference on Learning Representations, ICLR 2020

作者： Ruan, Yangjun Xiong, Yuanhao Reddi, Sashank Kumar, Sanjiv Hsieh, Cho-Jui Department of Infomation Science and Electrical Engineering Zhejiang University China Department of Computer Science UCLA United States Google Research

In the learning to learn (L2L) framework, we cast the design of optimization algorithms as a machine learning problem and use deep neural networks to learn the update rules. In this paper, we extend the L2L framework to zeroth-order (ZO) optimization setting, where no explicit gradient information is available. Our learned optimizer, modeled as recurrent neural networks (RNNs), first approximates gradient by ZO gradient estimator and then produces parameter update utilizing the knowledge of previous iterations. To reduce the high variance effect due to ZO gradient estimator, we further introduce another RNN to learn the Gaussian sampling rule and dynamically guide the query direction sampling. Our learned optimizer outperforms hand-designed algorithms in terms of convergence rate and final solution on both synthetic and practical ZO optimization problems (in particular, the black-box adversarial attack task, which is one of the most widely used applications of ZO optimization). We finally conduct extensive analytical experiments to demonstrate the effectiveness of our proposed optimizer. © 2020 8th International Conference on Learning Representations, ICLR 2020. All rights reserved.

关键词： Recurrent neural networks

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A Method for Soybean Germination Rate Detection Based on Image Processing

A Method for Soybean Germination Rate Detection Based on Ima...

引用

Annual Conference of Industrial Electronics Society

作者： Ru Han Lei Shu Menghan Yin Der-Jiunn Deng Kailiang Li Jin Zhang Xuefang Yi School of Artificial Intelligence Nanjing Agricultural University China NAU-Lincoln Joint Research Center of Intelligent Engineering Nanjing Agricultural University China School of Engineering University of Lincoln UK Department of Computer Science and Information Engineering National Changhua University of Education Taiwan China Telecom Hongxin Infomation Technology Company Nanjing

As the birthplace of soybeans, China has become the world's largest importer and consumer of soybeans. The supply and demand imbalance of soybeans has become one of the significant food and oil security concerns in *** solve this problem, this paper aims to study a soybean germination rate detection method based on image processing. By replacing manual detection of soybean germination rate and measuring soybean root length with machine-based approaches, basic image processing techniques including grayscale conversion, denoising, binarization, dilation, and erosion are applied to preprocess the images of soybean germination. An improved object detection algorithm is used to extract phenotypic features during soybean germination. The main focus of the study is twofold: (1) Measuring the seed area and the minimum bounding rectangle area of the image regions to determine the germination of seeds by comparing their ratio, thus obtaining the germination rate; (2) Using skeleton extraction and embryo-root separation methods to measure the changes in seed root length during germination, thereby selecting seeds with high vitality and improving the quality of soybean seeds.

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Learning to learn by zeroth-order oracle

arXiv

引用

arXiv 2019年

作者： Ruan, Yangjun Xiong, Yuanhao Reddi, Sashank Kumar, Sanjiv Hsieh, Cho-Jui Department of Infomation Science and Electrical Engineering Zhejiang University Department of Computer Science UCLA Google Research

In the learning to learn (L2L) framework, we cast the design of optimization algorithms as a machine learning problem and use deep neural networks to learn the update rules. In this paper, we extend the L2L framework to zeroth-order (ZO) optimization setting, where no explicit gradient information is available. Our learned optimizer, modeled as recurrent neural network (RNN), first approximates gradient by ZO gradient estimator and then produces parameter update utilizing the knowledge of previous iterations. To reduce high variance effect due to ZO gradient estimator, we further introduce another RNN to learn the Gaussian sampling rule and dynamically guide the query direction sampling. Our learned optimizer outperforms hand-designed algorithms in terms of convergence rate and final solution on both synthetic and practical ZO optimization tasks (in particular, the black-box adversarial attack task, which is one of the most widely used tasks of ZO optimization). We finally conduct extensive analytical experiments to demonstrate the effectiveness of our proposed optimizer. Copyright © 2019, The Authors. All rights reserved.

关键词： Recurrent neural networks

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A discount strategy in word-of-mouth marketing and its assessment

arXiv

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arXiv 2017年

作者： Zhang, Tianrui Yang, Xiaofan Yang, Lu-Xing Tang, Yuan Yan Wu, Yingbo College of Software Engineering Chongqing University Chongqing400044 China Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology DelftGA2600 Netherlands Department of Computer and Infomation Science University of Macau China

This paper addresses the discount pricing in word-of-mouth (WOM) marketing. A new discount strategy known as the Infection-Based Discount (IBD) strategy is proposed. The basic idea of the IBD strategy lies in that each customer enjoys a discount that is linearly proportional to his/her influence in the WOM network. To evaluate the performance of the IBD strategy, the WOM spreading process is modeled as a dynamic model known as the DPA model, and the performance of the IBD strategy is modeled as a function of the basic discount. Next, the influence of different factors, including the basic discount and the WOM network, on the dynamics of the DPA model is revealed experimentally. Finally, the influence of different factors on the performance of the IBD strategy is uncovered experimentally. On this basis, some promotional measures are recommended. Copyright © 2017, The Authors. All rights reserved.

关键词： Dynamic models

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