Audio forgery detection and localization with super resolution spectrogram and keypoint based clustering approach

Audio forgery detection and localization with super resolution spectrogram and keypoint based clustering approach | 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 Audio forgery detection and localization with super resolution spectrogram and keypoint based clustering approach Beste Ustubioglu, Gul Tahaoglu, Guzin Ulutas, Arda Ustubioglu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2534047/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Malicious individuals can modify speech recordings with advanced audio editing software to create forged audio. The most common forgery method, known as audio copy-move forgery, involves copying part of the audio to duplicate or delete a segment. Considering the fact that the speech recording is used as evidence in court, it is of great importance to detect whether the voice recordings are forged or not. To this end, we present an effective and robust method based on BRIEF and OPTICS to detect and locate audio copy-move forgeries. The proposed method uses super-resolution spectrogram images of the input audio to detect forged parts in suspicious audio recordings. For this purpose, key points and their feature descriptors are first extracted from the spectrogram image using the BRIEF method. The Ordering Points To Identify the Clustering Structure method (OPTICS) is used by the approach to match the corresponding descriptors. The proposed approach to eliminate false matches evaluates the correctness of the matches. The method also marks the corresponding forged segments in the audio file based on the location of the keypoints in these clusters. The performance results show that the proposed method has significantly high robustness to post-processing attacks such as noise addition, filtering, and especially compression, as reported in the literature. Audio copy-move-forgery detection Audio forgery Audio forensic high frequency spectrogram BRIEF feature clustering based matching Full Text Additional Declarations Competing interest reported. Dear Please find enclosed our joint paper titled “Audio forgery detection and localization with super resolution spectrogram and keypoint based clustering approach”. Our paper is original and has not been submitted anywhere else except in your journal. I hope it will meet your requirement for publication. Yours respectfully, Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 13 May, 2023 Reviews received at journal 25 Apr, 2023 Reviewers agreed at journal 24 Apr, 2023 Reviewers agreed at journal 24 Apr, 2023 Reviewers agreed at journal 23 Apr, 2023 Reviewers invited by journal 23 Apr, 2023 Editor assigned by journal 02 Feb, 2023 Submission checks completed at journal 02 Feb, 2023 First submitted to journal 31 Jan, 2023 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-2534047","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":172862377,"identity":"e6dc8fb5-f392-41f6-a658-81730c0af753","order_by":0,"name":"Beste Ustubioglu","email":"","orcid":"","institution":"Karadeniz Technical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Beste","middleName":"","lastName":"Ustubioglu","suffix":""},{"id":172862378,"identity":"8239147e-1fb5-4819-88c1-c057e0a48055","order_by":1,"name":"Gul Tahaoglu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYFAC5gYGxgYgzc7A+LABLJJASAtjYwNYCzMDs2FDAlTLASK1sEkSpUU+IrH9wc8ddnLyzjxmlTN/HGbgZ88xYP64B7cWwxuJjY29Z5KNDQ/zmN3ckHCYQbLnjQHDgWd4tMxIbGzgbWNO3NgM1PIAqMXgRg5QCx6XgbQ0/m2rB2spBGmxJ6RFXiKxsZm37XDifGYeM0aQwwwkCGgx4HnYOFu27bixATNbseSMtHQeiTPPCg6cwWdLe/KBj2/bquXk25s3fuyxsZbjb0/e+KACny0H0Bg8IAKPBqAtDeiMUTAKRsEoGAXoAABxiVmTFtS6mAAAAABJRU5ErkJggg==","orcid":"","institution":"Karadeniz Technical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Gul","middleName":"","lastName":"Tahaoglu","suffix":""},{"id":172862379,"identity":"e3a501c5-ed05-4be8-972b-889f5786f104","order_by":2,"name":"Guzin Ulutas","email":"","orcid":"","institution":"Karadeniz Technical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Guzin","middleName":"","lastName":"Ulutas","suffix":""},{"id":172862380,"identity":"1bc83c25-c42a-4ea3-aabf-f60c736d40a4","order_by":3,"name":"Arda Ustubioglu","email":"","orcid":"","institution":"Trabzon University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Arda","middleName":"","lastName":"Ustubioglu","suffix":""},{"id":172862381,"identity":"ce05d685-c046-4712-918f-f5bde848e329","order_by":4,"name":"Muhammed Kilic","email":"","orcid":"","institution":"Karadeniz Technical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Muhammed","middleName":"","lastName":"Kilic","suffix":""}],"badges":[],"createdAt":"2023-01-31 13:44:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2534047/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2534047/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":32527001,"identity":"73295590-cbe0-4fe4-bf5c-084741109711","added_by":"auto","created_at":"2023-02-06 13:16:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2952164,"visible":true,"origin":"","legend":"","description":"","filename":"BriefMakale.31.01.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2534047/v1_covered.pdf"}],"financialInterests":"Competing interest reported. Dear \n\nPlease find enclosed our joint paper titled “Audio forgery detection and localization with super resolution spectrogram and keypoint based clustering approach”. Our paper is original and has not been submitted anywhere else except in your journal. \nI hope it will meet your requirement for publication. \n\nYours respectfully,","formattedTitle":"Audio forgery detection and localization with super resolution spectrogram and keypoint based clustering approach","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":"the-journal-of-supercomputing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Journal of Supercomputing](https://www.springer.com/journal/11227)","snPcode":"11227","submissionUrl":"https://submission.nature.com/new-submission/11227/3","title":"The Journal of Supercomputing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Audio copy-move-forgery detection, Audio forgery, Audio forensic, high frequency spectrogram, BRIEF feature, clustering based matching ","lastPublishedDoi":"10.21203/rs.3.rs-2534047/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2534047/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMalicious individuals can modify speech recordings with advanced audio editing software to create forged audio. The most common forgery method, known as audio copy-move forgery, involves copying part of the audio to duplicate or delete a segment. Considering the fact that the speech recording is used as evidence in court, it is of great importance to detect whether the voice recordings are forged or not. To this end, we present an effective and robust method based on BRIEF and OPTICS to detect and locate audio copy-move forgeries. The proposed method uses super-resolution spectrogram images of the input audio to detect forged parts in suspicious audio recordings. For this purpose, key points and their feature descriptors are first extracted from the spectrogram image using the BRIEF method. The Ordering Points To Identify the Clustering Structure method (OPTICS) is used by the approach to match the corresponding descriptors. The proposed approach to eliminate false matches evaluates the correctness of the matches. The method also marks the corresponding forged segments in the audio file based on the location of the keypoints in these clusters. The performance results show that the proposed method has significantly high robustness to post-processing attacks such as noise addition, filtering, and especially compression, as reported in the literature.\u003c/p\u003e","manuscriptTitle":"Audio forgery detection and localization with super resolution spectrogram and keypoint based clustering approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-02-06 13:15:41","doi":"10.21203/rs.3.rs-2534047/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-05-13T21:50:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-04-25T06:49:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"0a2ca260-7878-4289-938b-09ff478f9f1e","date":"2023-04-24T05:08:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"c0fa3537-e30b-49d5-975a-ddb897eaa75a","date":"2023-04-24T04:01:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"618a2bd3-8cca-4b29-a2f7-825c4ad07e91","date":"2023-04-24T01:35:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-04-23T20:53:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-02-02T16:34:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-02-02T16:34:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"The Journal of Supercomputing","date":"2023-01-31T13:42:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-journal-of-supercomputing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Journal of Supercomputing](https://www.springer.com/journal/11227)","snPcode":"11227","submissionUrl":"https://submission.nature.com/new-submission/11227/3","title":"The Journal of Supercomputing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9768541f-0364-4a47-9f93-b02c90041ff5","owner":[],"postedDate":"February 6th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2023-06-14T00:44:14+00:00","versionOfRecord":[],"versionCreatedAt":"2023-02-06 13:15:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2534047","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2534047","identity":"rs-2534047","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}