Contrastive Domain Adaptation for Authenticity Detection in Chat Record Screenshots

Contrastive Domain Adaptation for Authenticity Detection in Chat Record Screenshots | 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 Contrastive Domain Adaptation for Authenticity Detection in Chat Record Screenshots Congcong Huo, Jinhe Wu, Hongji Liu, Shunquan Tan, Zhenjun Li, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6321833/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Malicious actors use realistic social software interface simulators to forge deceptive screenshots, spread rumors, and manipulate public opinion, which seriously disrupts social order. At present, the verification of the authenticity of screenshots mainly relies on specialists' review of complex details such as icon size and text spacing. If the screenshots come from different devices, it will exponentially increase the difficulty of expert identification. In response to the issue of discerning the authenticity of chat record screenshots across various devices, we propose an automated framework and detection model, which aims to maintain the accuracy and efficiency in discerning the authenticity of chat record screenshots from different devices. We collect sample data from different devices by generating various text conversations through ChatGPT. We designed a solution that combines cross-domain learning and contrastive learning concepts. By fine-tuning with two loss functions, the model has improved its performance in identifying same-category samples across different device domains and different-category samples within the same device domain. The model has a detection accuracy of 98% for chat record screenshots with high reliability. Chat record screenshots Authenticity detection Contrastive domain adaptation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 31 Mar, 2025 Reviewers invited by journal 31 Mar, 2025 Editor assigned by journal 27 Mar, 2025 Submission checks completed at journal 27 Mar, 2025 First submitted to journal 27 Mar, 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. 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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-6321833","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":436600499,"identity":"799932af-a1e8-49b4-824f-20d8869f8123","order_by":0,"name":"Congcong Huo","email":"","orcid":"","institution":"Shenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Congcong","middleName":"","lastName":"Huo","suffix":""},{"id":436600503,"identity":"b528b662-cfb4-40f5-a224-de35f5764811","order_by":1,"name":"Jinhe Wu","email":"","orcid":"","institution":"Guangzhou College of 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