A Hybrid Ensemble Approach for Robust Detection of Adversarial Attacks on Medical X-ray Images

A Hybrid Ensemble Approach for Robust Detection of Adversarial Attacks on Medical X-ray Images | 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 Hybrid Ensemble Approach for Robust Detection of Adversarial Attacks on Medical X-ray Images Yassine Chahid, Anas Chahid, Ismail Chahid, Aissa Kerkour Elmiad This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7620818/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 Medical imaging systems driven by AI are revolutionizing diagnostics, but they are still susceptible to adversarial attacks, which are small, purposefully designed disruptions that can trick algorithms and impair clinical judgment. Therefore, creating strong detection methods is essential to guaranteeing patient safety and preserving confidence in AI-assisted diagnostics \cite{nasim2024ai}. In this work, we develop and thoroughly test three methods for identifying adversarial perturbations in X-ray images, a Random Forest (RF), a Convolutional Neural Network (CNN), and a Hybrid Ensemble model that makes use of their complementary advantages. A carefully selected dataset of 12,677 X-ray images with different perturbation strengths (ϵ) was used to test the models. Under moderate attack conditions (ϵ = 0.02), the Hybrid Ensemble consistently outperformed the standalone models, achieving an accuracy of 97.4%. Crucially, it reduced the most critical errors, or false negatives, to just 15, as opposed to 38 for the Random Forest. Additionally, the ensemble showed better resilience, sustaining a high F1-Score of 97.4% in the face of attacks (ϵ = 0.03), in which case the RF's performance declined noticeably. The suggested Hybrid Ensemble provides a strong, dependable, and clinically applicable way to improve the security and credibility of AI in medical imaging by skillfully combining the CNN's spatial feature learning with the RF's sensitivity to statistical anomalies. Adversarial attacks Medical imaging Hybrid Ensemble X-ray diagnostics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 21 Sep, 2025 Reviewers invited by journal 20 Sep, 2025 Editor assigned by journal 16 Sep, 2025 Submission checks completed at journal 16 Sep, 2025 First submitted to journal 15 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-7620818","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":519212381,"identity":"c84eb2a9-81a6-440f-821b-655b90c655ac","order_by":0,"name":"Yassine 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Images","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"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 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Therefore, creating strong detection methods is essential to guaranteeing patient safety and preserving confidence in AI-assisted diagnostics \\cite{nasim2024ai}. In this work, we develop and thoroughly test three methods for identifying adversarial perturbations in X-ray images, a Random Forest (RF), a Convolutional Neural Network (CNN), and a Hybrid Ensemble model that makes use of their complementary advantages. A carefully selected dataset of 12,677 X-ray images with different perturbation strengths (ϵ) was used to test the models. Under moderate attack conditions (ϵ = 0.02), the Hybrid Ensemble consistently outperformed the standalone models, achieving an accuracy of 97.4%. Crucially, it reduced the most critical errors, or false negatives, to just 15, as opposed to 38 for the Random Forest. Additionally, the ensemble showed better resilience, sustaining a high F1-Score of 97.4% in the face of attacks (ϵ = 0.03), in which case the RF's performance declined noticeably. The suggested Hybrid Ensemble provides a strong, dependable, and clinically applicable way to improve the security and credibility of AI in medical imaging by skillfully combining the CNN's spatial feature learning with the RF's sensitivity to statistical anomalies.\u003c/p\u003e","manuscriptTitle":"A Hybrid Ensemble Approach for Robust Detection of Adversarial Attacks on Medical X-ray Images","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-01 08:41:43","doi":"10.21203/rs.3.rs-7620818/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"310455348532606442415292186113326555240","date":"2025-09-21T11:30:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-21T02:59:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-16T17:17:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-16T17:17:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Signal, Image and Video Processing","date":"2025-09-15T12:41:35+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":"c23c492b-bd93-4d49-8a2f-ab2c17dd5a80","owner":[],"postedDate":"October 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-01T08:41:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-01 08:41:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7620818","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7620818","identity":"rs-7620818","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}