Advanced Cyber Security Framework for Fingerprint and Biometric Authentication Against Adversarial Attacks

Recent advances in deep learning have significantly improved biometric authentication systems, while simultaneously introducing new vulnerabilities to adversarial attacks. Earlier studies mainly focused on specific attack techniques or isolated system components, often neglecting interactions across the authentication pipeline. A scenario-based risk assessment framework has therefore been proposed to analyze vulnerabilities in deep learning–driven biometric systems. This framework identifies several critical points within the authentication process and outlines multiple attack scenarios categorized by objectives such as integrity, availability, and privacy, as well as by attack strategies including evasion, poisoning, and exploratory methods. Biometric authentication is increasingly used in commercial, governmental, and forensic applications; however, it remains vulnerable to both intrinsic failures and adversarial manipulation. A metadata-based approach that integrates user and acquisition information has demonstrated improved system performance, increasing recognition accuracy and robustness. Furthermore, smartphone fingerprint authentication systems exhibit architectural weaknesses that may allow brute-force and man-in-the-middle attacks. Side-channel information leakage can also expose sensitive biometric data, highlighting the need for stronger security mechanisms.