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An Architecture for Voice-Based Authentication and Authorization with Deepfake Detection

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Publicado en:European Conference on Cyber Warfare and Security (Jun 2025), p. 425-436
Autor principal: da Silva, Fabian Martins
Otros Autores: Balamurugan, Baladithya, Hakim, John
Publicado:
Academic Conferences International Limited
Materias:
Forgery
Web services
Deepfake
Smartphones
Computer architecture
Biometrics
Identification
Passwords
Neural networks
Deception
Facial recognition technology
Real time
Authentication
Access control
Voice
Cost effectiveness
Authenticity
Suitability
Effectiveness
Models
Efficiency
Deployment
Resilience
Transactions
Cost analysis
Verification
Voice recognition
Architecture
Authorization
Banking
Unauthorized
Acceso en línea:Citation/Abstract
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Resumen:Voice biometrics offer a convenient and secure authentication method, but the rise of sophisticated deepfake technology presents a significant challenge. This work presents an architecture for voice-based authentication and authorization that integrates deepfake detection to mitigate this risk. This paper explores the design of this cloud-native architecture, leveraging Amazon Web Services (AWS) services for orchestration and scalability. The system combines cutting-edge Al models for robust voice-printing and real-time deepfake analysis. We discuss multi-factor authentication (MFA) strategies that provide layered defense against unauthorized access. Two specific use cases are explored: identity verification and secure approval of banking transactions. This paper addresses key considerations for real-world deployment, including system resiliency, cost-effectiveness, and the efficiency of the Al models under varying conditions. We evaluate the architecture's suitability as a two-factor authentication (2FA) solution, focusing on the accuracy of deepfake detection and the rates of false negatives and false positives.
Fuente:Political Science Database