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The methodology involves developing separate CNN models for subject ID and finger number recognition, which are then combined to create a unified fingerprint identification model. Utilizing the SOCOFing dataset, the models achieve high accuracy, with subject ID recognition reaching 99.73% and finger number classification achieving 99.83%. Furthermore, FGSM analysis reveals a decrease in accuracy with increasing epsilon values, indicating the model\u2019s sensitivity to adversarial attacks. Overall, this method shows a promising solution for improving the performance and security of fingerprint recognition systems (FRSs), thereby advancing biometric authentication technologies in various domains.<\/jats:p>","DOI":"10.1142\/s0219649226500012","type":"journal-article","created":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T04:03:08Z","timestamp":1769400188000},"source":"Crossref","is-referenced-by-count":0,"title":["Securing Biometric Systems: Dual CNNs Empowered with FGSM Analysis for Fingerprint Recognition"],"prefix":"10.1142","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1296-9620","authenticated-orcid":false,"given":"R.","family":"Sreemol","sequence":"first","affiliation":[{"name":"Department of Computer Applications, Cochin University of Science and Technology, Kochi, Kerala, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1893-8414","authenticated-orcid":false,"given":"M. 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