Optimally Configured Generative Adversarial Networks to Distinguish Real and AI- Generated Human Faces

Optimally Configured Generative Adversarial Networks to Distinguish Real and AI- Generated Human Faces | 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 Optimally Configured Generative Adversarial Networks to Distinguish Real and AI- Generated Human Faces Kalaimani G, Kavitha G, Selvan Chinnaiyan, Srikanth Mylapalli This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4107900/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Jul, 2024 Read the published version in Signal, Image and Video Processing → Version 1 posted 8 You are reading this latest preprint version Abstract Artificial Intelligence (AI) has come a long way in the last several years, especially in terms of producing human-like faces with deep-fake technology. However, the challenge lies in accurately distinguishing between real and AI-generated human faces. As the applications of such technology continue to expand, the need for robust classification methods becomes crucial to ensure ethical and responsible use. Existing Generative Adversarial Networks (GANs) produce increasingly realistic synthetic faces, making it difficult for traditional methods to differentiate between real and generated faces. This poses potential risks in various domains, including security, identity verification, and misinformation. The primary objective of this research is to design an optimally configured GAN capable of distinguishing between real and generated faces and to develop a robust classifier that accurately classifies human faces as either real or generative. The results showcase the effectiveness of the optimally configured GAN model in achieving high accuracy, reaching 95%, in distinguishing between real and AI-generated faces across state-of-the-art techniques. The research contributes to the ethical deployment of AI technologies, safeguards security applications, strengthens identity verification systems, combats misinformation, and fosters public trust in the era of advanced AI. Artificial Intelligence Generative Adversarial Networks real and AI-generated human face optimal configuration Lyrebird Optimization Algorithm (LOA) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 31 Jul, 2024 Read the published version in Signal, Image and Video Processing → Version 1 posted Editorial decision: Revision requested 25 Jun, 2024 Reviewers agreed at journal 02 Apr, 2024 Reviews received at journal 21 Mar, 2024 Reviewers agreed at journal 18 Mar, 2024 Reviewers invited by journal 18 Mar, 2024 Editor assigned by journal 15 Mar, 2024 Submission checks completed at journal 15 Mar, 2024 First submitted to journal 15 Mar, 2024 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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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4107900","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":280841377,"identity":"6dc1e054-6caf-4b1e-8268-b2522654e735","order_by":0,"name":"Kalaimani G","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYBACCQbmBoYEIIONmYGNgacCyGIGiuDXwois5QxICyMRWqCAjYG3DUQT0CLZfrDxw8Mcuzw+dt5jD97Oq43mbwdq+VGxDacWaZ7EZonEbcnFbMx86YZztx3PnXGYsYGx58xtnFrkGBIbgFqYE9uYecykebcdy20AamFmbMOjhf9h84/EbfVQLXOO5c4npEVaIrENaMthqJaGmtwNhLRIznjYZpG47ThQC1+a5JxjB3I3ArUcxOcXifPJh2/+3FadOL//7DGJNzV1ufPOHz744EcFbi1IgAdEHAYzDxCjHqaljkjFo2AUjIJRMJIAAGcfWOcZAFAEAAAAAElFTkSuQmCC","orcid":"","institution":"Karpagam College of Engineering","correspondingAuthor":true,"prefix":"","firstName":"Kalaimani","middleName":"","lastName":"G","suffix":""},{"id":280841378,"identity":"53e29de3-291f-42b1-8300-6daef4277207","order_by":1,"name":"Kavitha G","email":"","orcid":"","institution":"Muthayammal Engineering College","correspondingAuthor":false,"prefix":"","firstName":"Kavitha","middleName":"","lastName":"G","suffix":""},{"id":280841379,"identity":"0f0a130d-867f-497e-98b8-de4727aeb7a6","order_by":2,"name":"Selvan Chinnaiyan","email":"","orcid":"","institution":"REVA University","correspondingAuthor":false,"prefix":"","firstName":"Selvan","middleName":"","lastName":"Chinnaiyan","suffix":""},{"id":280841380,"identity":"2f9c57e2-8260-440c-bf2e-e8468959f0c1","order_by":3,"name":"Srikanth Mylapalli","email":"","orcid":"","institution":"Koneru Lakshmaiah Education Foundation","correspondingAuthor":false,"prefix":"","firstName":"Srikanth","middleName":"","lastName":"Mylapalli","suffix":""}],"badges":[],"createdAt":"2024-03-15 12:45:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4107900/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4107900/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11760-024-03440-6","type":"published","date":"2024-07-31T15:57:44+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":61793967,"identity":"9bfdf636-b3ce-47f2-910e-61379cdf8774","added_by":"auto","created_at":"2024-08-05 16:17:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1702239,"visible":true,"origin":"","legend":"","description":"","filename":"Paper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4107900/v1_covered_12fbaf6d-a98f-459c-91e0-3960211c91f4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimally Configured Generative Adversarial Networks to Distinguish Real and AI- Generated Human Faces","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"signal-image-and-video-processing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sivp","sideBox":"Learn more about [Signal, Image and Video Processing](http://link.springer.com/journal/11760)","snPcode":"11760","submissionUrl":"https://submission.nature.com/new-submission/11760/3","title":"Signal, Image and Video Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Artificial Intelligence, Generative Adversarial Networks, real and AI-generated human face, optimal configuration, Lyrebird Optimization Algorithm (LOA)","lastPublishedDoi":"10.21203/rs.3.rs-4107900/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4107900/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eArtificial Intelligence (AI) has come a long way in the last several years, especially in terms of producing human-like faces with deep-fake technology. 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