Leveraging Convolutional and Transformer Synergy for Robust Camera Source Attribution

Leveraging Convolutional and Transformer Synergy for Robust Camera Source Attribution | 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 Leveraging Convolutional and Transformer Synergy for Robust Camera Source Attribution Kunal Roy, Ruchira Naskar, Indrajit Banerjee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7741245/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract The majority of contemporary communication relies on electronic devices. Digital images and videos are regarded the primary modes of modern communication.Identifying the source camera model is crucial in digital forensics, having applications in authenticity verification and copyright protection.Traditional deep learning-based techniques to recognizing the validity of a digital source in the field of digital forensics have grown in prominence over the previous decade.In light of the necessity for optimizing model complexity and enhancing performance on this topic, we explore video spectrum and spectral information analysis to provide an effective solution for video source identification. In this work, we propose a scalable approach for device identification that uses spectrum images extracted from video frames using Fast Fourier Transform (FFT) and feeds them into a hybrid Convolutional Neural Network (CNN)-based Transformer model.The proposed approach has also been compared to state-of-the-art source camera detection technologies. The experimental findings illustrate the efficacy and advantages of the proposed system in terms of accuracy and robustness. Here, we contribute to both local and global representation learning with the help of self-attention mechanism. The overall accuracy of the proposed model achieved is over 98%. Multimedia forensics Self-Attention mechanism Source camera identification Source detection Spectrum images Transformer Architecture Video forensics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 11 Oct, 2025 Editor assigned by journal 05 Oct, 2025 Submission checks completed at journal 05 Oct, 2025 First submitted to journal 29 Sep, 2025 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7741245","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533090798,"identity":"96a54b6f-6620-4475-af79-a3ee2b8257c1","order_by":0,"name":"Kunal 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Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Multimedia forensics, Self-Attention mechanism, Source camera identification, Source detection, Spectrum images, Transformer Architecture, Video forensics","lastPublishedDoi":"10.21203/rs.3.rs-7741245/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7741245/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe majority of contemporary communication relies on electronic devices. Digital images and videos are regarded the primary modes of modern communication.Identifying the source camera model is crucial in digital forensics, having applications in authenticity verification and copyright protection.Traditional deep learning-based techniques to recognizing the validity of a digital source in the field of digital forensics have grown in prominence over the previous decade.In light of the necessity for optimizing model complexity and enhancing performance on this topic, we explore video spectrum and spectral information analysis to provide an effective solution for video source identification. In this work, we propose a scalable approach for device identification that uses spectrum images extracted from video frames using Fast Fourier Transform (FFT) and feeds them into a hybrid Convolutional Neural Network (CNN)-based Transformer model.The proposed approach has also been compared to state-of-the-art source camera detection technologies. The experimental findings illustrate the efficacy and advantages of the proposed system in terms of accuracy and robustness. Here, we contribute to both local and global representation learning with the help of self-attention mechanism. The overall accuracy of the proposed model achieved is over 98%.\u003c/p\u003e","manuscriptTitle":"Leveraging Convolutional and Transformer Synergy for Robust Camera Source Attribution","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-24 03:44:59","doi":"10.21203/rs.3.rs-7741245/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-10-12T02:03:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-06T03:02:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-06T03:02:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Signal, Image and Video Processing","date":"2025-09-29T10:37:14+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"48d60396-9e02-4510-9653-7518fd886e4f","owner":[],"postedDate":"October 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-24T03:45:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-24 03:44:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7741245","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7741245","identity":"rs-7741245","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}