RADIC Voice Authentication: Replay Attack Detection using Image Classification for Voice Authentication Systems
Honors in Business
Date of Award
Ghaith H. Husari
Thesis Professor Department
Computer and Information Sciences
Phillip E. Pfeiffer, Mathew R. Desjardins
Systems like Google Home, Alexa, and Siri that use voice-based authentication to verify their users’ identities are vulnerable to voice replay attacks. These attacks gain unauthorized access to voice-controlled devices or systems by replaying recordings of passphrases and voice commands. This shows the necessity to develop more resilient voice-based authentication systems that can detect voice replay attacks.
This thesis implements a system that detects voice-based replay attacks by using deep learning and image classification of voice spectrograms to differentiate between live and recorded speech. Tests of this system indicate that the approach represents a promising direction for detecting voice-based replay attacks.
East Tennessee State University
Honors Thesis - Open Access
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.
Taylor, Hannah, "RADIC Voice Authentication: Replay Attack Detection using Image Classification for Voice Authentication Systems" (2023). Undergraduate Honors Theses. Paper 782. https://dc.etsu.edu/honors/782
Copyright by the authors.