Honors Program

Honors in Business

Date of Award


Thesis Professor(s)

Ghaith H. Husari

Thesis Professor Department

Computer and Information Sciences

Thesis Reader(s)

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

Document Type

Honors Thesis - Open Access

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.


Copyright by the authors.