An Architecture for Voice-Based Authentication and Authorization with Deepfake Detection
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| Publicado no: | European Conference on Cyber Warfare and Security (Jun 2025), p. 425-436 |
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| Autor principal: | |
| Outros Autores: | , |
| Publicado em: |
Academic Conferences International Limited
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| Acesso em linha: | Citation/Abstract Full Text Full Text - PDF |
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| 035 | |a 3244089539 | ||
| 045 | 2 | |b d20250601 |b d20250630 | |
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| 100 | 1 | |a da Silva, Fabian Martins | |
| 245 | 1 | |a An Architecture for Voice-Based Authentication and Authorization with Deepfake Detection | |
| 260 | |b Academic Conferences International Limited |c Jun 2025 | ||
| 513 | |a Conference Proceedings | ||
| 520 | 3 | |a 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. | |
| 653 | |a Forgery | ||
| 653 | |a Web services | ||
| 653 | |a Deepfake | ||
| 653 | |a Smartphones | ||
| 653 | |a Computer architecture | ||
| 653 | |a Biometrics | ||
| 653 | |a Identification | ||
| 653 | |a Passwords | ||
| 653 | |a Neural networks | ||
| 653 | |a Deception | ||
| 653 | |a Facial recognition technology | ||
| 653 | |a Real time | ||
| 653 | |a Authentication | ||
| 653 | |a Access control | ||
| 653 | |a Voice | ||
| 653 | |a Cost effectiveness | ||
| 653 | |a Authenticity | ||
| 653 | |a Suitability | ||
| 653 | |a Effectiveness | ||
| 653 | |a Models | ||
| 653 | |a Efficiency | ||
| 653 | |a Deployment | ||
| 653 | |a Resilience | ||
| 653 | |a Transactions | ||
| 653 | |a Cost analysis | ||
| 653 | |a Verification | ||
| 653 | |a Voice recognition | ||
| 653 | |a Architecture | ||
| 653 | |a Authorization | ||
| 653 | |a Banking | ||
| 653 | |a Unauthorized | ||
| 700 | 1 | |a Balamurugan, Baladithya | |
| 700 | 1 | |a Hakim, John | |
| 773 | 0 | |t European Conference on Cyber Warfare and Security |g (Jun 2025), p. 425-436 | |
| 786 | 0 | |d ProQuest |t Political Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3244089539/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3244089539/fulltext/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3244089539/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |