Securing the Diagnosis of Medical Imaging: An In-depth Analysis of AI-Resistant Attacks

Securing the Diagnosis of Medical Imaging: An In-depth Analysis of AI-Resistant Attacks | 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 Securing the Diagnosis of Medical Imaging: An In-depth Analysis of AI-Resistant Attacks Angona Biswas, Abdullah Al Nasim, Kishor Datta Gupta, Roy George, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3924726/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 Machine learning (ML) is a rapidly developing area of medicine that uses significant resources to apply computer science and statistics to medical issues. ML's proponents laud its capacity to handle vast, complicated, and erratic medical data. It's common knowledge that attackers might cause misclassification by deliberately creating inputs for machine learning classifiers. Research on adversarial examples has been extensively conducted in the field of computer vision applications. Healthcare systems are thought to be highly difficult because of the security and life-or-death considerations they include, and performance accuracy is very important. Recent arguments have suggested that adversarial attacks could be made against medical image analysis (MedIA) technologies because of the accompanying technology infrastructure and powerful financial incentives. Since the diagnosis will be the basis for important decisions, it is essential to assess how strong medical DNN tasks are against adversarial attacks. Simple adversarial attacks have been taken into account in several earlier studies. However, DNNs are susceptible to more risky and realistic attacks. The present paper covers recent proposed adversarial attack strategies against DNNs for medical imaging as well as countermeasures. In this study, we review current techniques for adversarial imaging attacks, detections. It also encompasses various facets of these techniques and offers suggestions for the robustness of neural networks to be improved in the future. Adversarial attack Medical image Deep Neural Network Model safety Robustness 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. 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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-3924726","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":271098923,"identity":"ab94a705-e1a6-4320-b43b-35acfce06f9f","order_by":0,"name":"Angona Biswas","email":"","orcid":"","institution":"Pioneer Alpha","correspondingAuthor":false,"prefix":"","firstName":"Angona","middleName":"","lastName":"Biswas","suffix":""},{"id":271098924,"identity":"12055349-9be3-420d-b5ea-4171b868693b","order_by":1,"name":"Abdullah Al Nasim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYJCCA0CcwHCAufEAY4MNDylaGBuAKI04LQxIWg4TVmrOfvzhoRsMdnl8NxIbDvzccV6Gn735AMPHPbU4tVj25BgczmFILpYEajnYe+Y2j2TPsQTGGc+O49RicCCHAaiFOXEDyBbetts8BjdyDJh5DhzDreX88wdALfVgLQf/tp3jsb///gN+LTcSQA47DNZymLftAI+BBA8DUEsNHi1vgFoMjhdLnnnYcFi2LZlH4kyawcEZBw7gcVj64885FdV5fMeTDz5822Znz99++OGDDwfqcGqBakTjA60gIoLQASFbRsEoGAWjYAQBAMsFZwhf2n+JAAAAAElFTkSuQmCC","orcid":"","institution":"Pioneer Alpha","correspondingAuthor":true,"prefix":"","firstName":"Abdullah","middleName":"Al","lastName":"Nasim","suffix":""},{"id":271098925,"identity":"d807309b-0fa4-467e-8540-6c92cadc3902","order_by":2,"name":"Kishor Datta Gupta","email":"","orcid":"","institution":"Clark Atlanta University","correspondingAuthor":false,"prefix":"","firstName":"Kishor","middleName":"Datta","lastName":"Gupta","suffix":""},{"id":271098926,"identity":"3516be04-03ab-43d9-a77f-47b678143ada","order_by":3,"name":"Roy George","email":"","orcid":"","institution":"Clark Atlanta University","correspondingAuthor":false,"prefix":"","firstName":"Roy","middleName":"","lastName":"George","suffix":""},{"id":271098927,"identity":"d3e8e185-5bdc-43fd-a6da-410d7c0f4bf1","order_by":4,"name":"Khalil Shujaee","email":"","orcid":"","institution":"Clark Atlanta University","correspondingAuthor":false,"prefix":"","firstName":"Khalil","middleName":"","lastName":"Shujaee","suffix":""},{"id":271098928,"identity":"c066c0d9-4f72-4bff-8674-8cc99a5e5a22","order_by":5,"name":"Abdur Rashid","email":"","orcid":"","institution":"Westcliff University","correspondingAuthor":false,"prefix":"","firstName":"Abdur","middleName":"","lastName":"Rashid","suffix":""}],"badges":[],"createdAt":"2024-02-03 17:00:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3924726/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3924726/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51595094,"identity":"b3341eb3-6ce0-4092-93d9-bcbd52b7be68","added_by":"auto","created_at":"2024-02-25 04:37:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1369010,"visible":true,"origin":"","legend":"","description":"","filename":"Journal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3924726/v1_covered_5ecc4457-ef02-4280-9092-1350eb02f4ae.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Securing the Diagnosis of Medical Imaging: An In-depth Analysis of AI-Resistant Attacks","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":"Adversarial attack, Medical image, Deep Neural Network, Model safety, Robustness","lastPublishedDoi":"10.21203/rs.3.rs-3924726/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3924726/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMachine learning (ML) is a rapidly developing area of medicine that uses significant resources to apply computer science and statistics to medical issues. 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