<![CDATA[The rapid advancement of deepfake technology powered by Generative AI has created a critical security threat to biometric authentication systems, particularly face recognition and voice recognition. Global reports from 2023–2024 indicate a dramatic 3,000% increase in deepfake-enabled identity fraud, emphasizing the urgent need for stronger biometric security measures. This study aims to analyze key vulnerabilities in biometric systems against deepfake attacks, evaluate the effectiveness of existing detection techniques, and develop a layered biometric security framework as a mitigation solution. The research employs a Systematic Literature Review (SLR) of scientific publications and industry reports from 2016–2025, complemented by Lite Expert Validation to assess the feasibility of the proposed framework. The findings reveal that most biometric systems remain vulnerable to advanced face swap, voice cloning, and liveness detection bypass attacks. Detection methods based on frequency–spatial analysis and multi-modal deepfake detection are identified as the most effective, although they require strong operational integration. This study introduces the Biometric Deepfake Security Framework, which incorporates technical, procedural, and adaptive security controls to enhance biometric resilience against digital identity manipulation. The proposed framework is expected to provide practical guidance for organizations relying on biometric authentication to strengthen protection against evolving deepfake threats.]]>
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