A Robust Image Forgery Detection and Localization Approach based on Context-Aware Attention Pooling and Convolutional Block Attention Module for Improved Detection Performance

A Robust Image Forgery Detection and Localization Approach based on Context-Aware Attention Pooling and Convolutional Block Attention Module for Improved Detection Performance | 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 A Robust Image Forgery Detection and Localization Approach based on Context-Aware Attention Pooling and Convolutional Block Attention Module for Improved Detection Performance Debolina Ghosh, Ruchira Naskar, Bidesh Chakraborty This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5415763/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 Image manipulation technology has emerged and developed quickly, posing a threat to many facets of our society. Consequently, the identification of picture alteration has become more crucial. Though considerable progress has been made, past approaches to forgery detection did not account for the differences in the size of the tampered areas in each fake image. In this research, we argue that the primary cause of the low precision is the network’s incapacity to handle tampering regions of different sizes. We suggest Context-Aware Attentional pooling-based U-Net structures because of their simplicity in implementation, ease of integration , emphasis on feature relevance, scalability, noise reduction, and computing efficiency. It extends the capabilities of the U-Net by incorporating residual propagation and feedback, an attention gate, and Context-aware Attentional pooling (CAP) with Convolutional Block Attention Module (CBAM). The concept of channel mixing is larger in CBAM, which may indicate a more integrated method of managing spatial information and channel dependencies. In order to maximise 1 feature extraction, multiscale context understanding, and ultimately more accurate and dependable forensic analysis, spatial attention, channel attention, and Context-Aware Attentional Pooling (CAP) are integrated into image forensics. This model’s inclusion of Context-Aware Attentional Pooling (CAP) and Channel Attention (CA) improves its robustness against noise and compression, and improves detection accuracy and localisation with both global and local context, making it perform better than other state-of-the-art models. This combination is a potent method in the field of image forensics since it is very good at identifying subtle and intricate image manipulation. Forgery detection Context-Aware Attentional Pooling U-Net Convolutional Block Attention Module Splicing detection Full Text Additional Declarations No competing interests reported. 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. 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-5415763","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":375830703,"identity":"25e3d227-216b-4f1e-a906-55e2977fd4b9","order_by":0,"name":"Debolina Ghosh","email":"data:image/png;base64,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","orcid":"","institution":"Indian Institute of Engineering Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Debolina","middleName":"","lastName":"Ghosh","suffix":""},{"id":375830706,"identity":"be58f132-d58b-42e1-985f-7828130421a1","order_by":1,"name":"Ruchira Naskar","email":"","orcid":"","institution":"Indian Institute of Engineering Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ruchira","middleName":"","lastName":"Naskar","suffix":""},{"id":375830707,"identity":"d398528a-6374-498e-b692-23b3eb86d8a8","order_by":2,"name":"Bidesh Chakraborty","email":"","orcid":"","institution":"Haldia Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Bidesh","middleName":"","lastName":"Chakraborty","suffix":""}],"badges":[],"createdAt":"2024-11-08 10:23:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5415763/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5415763/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72522453,"identity":"353c322b-5439-4bfc-9b0d-54d5dbaa8592","added_by":"auto","created_at":"2024-12-28 14:16:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":750086,"visible":true,"origin":"","legend":"","description":"","filename":"SplicedUNet.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5415763/v1_covered_4e738cd8-ebde-4788-a0d6-2d35a24d2a34.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Robust Image Forgery Detection and Localization Approach based on Context-Aware Attention Pooling and Convolutional Block Attention Module for Improved Detection Performance","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Forgery detection, Context-Aware Attentional Pooling, U-Net, Convolutional Block Attention Module, Splicing detection","lastPublishedDoi":"10.21203/rs.3.rs-5415763/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5415763/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Image manipulation technology has emerged and developed quickly, posing a threat to many facets of our society. Consequently, the identification of picture alteration has become more crucial. Though considerable progress has been made, past approaches to forgery detection did not account for the differences in the size of the tampered areas in each fake image. In this research, we argue that the primary cause of the low precision is the network’s incapacity to handle tampering regions of different sizes. We suggest Context-Aware Attentional pooling-based U-Net structures because of their simplicity in implementation, ease of integration , emphasis on feature relevance, scalability, noise reduction, and computing efficiency. It extends the capabilities of the U-Net by incorporating residual propagation and feedback, an attention gate, and Context-aware Attentional pooling (CAP) with Convolutional Block Attention Module (CBAM). The concept of channel mixing is larger in CBAM, which may indicate a more integrated method of managing spatial information and channel dependencies. 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