An Efficient Deepfake Detection System Using ConvoReinAutoNet and GeoFisherNet

An Efficient Deepfake Detection System Using ConvoReinAutoNet and GeoFisherNet | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article An Efficient Deepfake Detection System Using ConvoReinAutoNet and GeoFisherNet Dr. Azan Hamad ALkhorem This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5691215/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study suggests a hybrid optimization model and a new deep learning technique to create an effective deepfake detection system. During the preprocessing stage, deepfake database images are improved by employing Gaussian Filter and Histogram Equalization and are ready for analysis. The recently proposed Improved Local Ternary Patterns (I-LTP) approach collects textural and temporal information for feature extraction. The advanced GeoFisherNet, which effectively integrates spatial and temporal properties, is then utilized to fuse these data. The Marine Predator Customized White Shark Optimizer (MCWO), a hybrid approach that combines the White Shark Optimization Algorithm (WSO) and Marine Predator Algorithm (MPA), is used to find the most discriminative features during the feature selection phase. Lastly, ConvoReinAutoNet (CRAN), a revolutionary deep learning architecture that combines Convolutional Neural Networks (CNN), Deep Reinforcement Learning (DRL), and Autoencoders, is applied to the fused and optimized features in the classification phase to make precise detection decisions. The Python implementation of the suggested system shows better detection efficiency and accuracy of two data splits (70% and 80%) are 98.78% and 99.42% than the current methods. Deepfake Detection Cybersecurity Hybrid Optimization Ensemble Deep Learning Full Text Additional Declarations The authors declare potential competing interests as follows: Department of Computer Engineering, College of Computer Science and Information Technology, Majmaah University, Majmaah, 11952, Saudi Arabia Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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