Detection of impostor and tampered segments in audio by using an intelligent system

Zeshan Mubeen, Mehtab Afzal, Zulfiqar Ali*, Suleman Khan, Muhammad Imran

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The transmission of audio data via the Internet of Things makes such data vulnerable to tampering. Moreover, the availability of sophisticated tampering tools has allowed mobsters to change the context of audio data by altering their segments. Tampered audio may result in unpleasant situations for any member of society. To avoid such circumstances, a new audio forgery detection system is proposed in this study. This system can be deployed in edge devices to identify impostors and tampering in audio data. The proposed system is implemented using state-of-the-art mel-frequency cepstral coefficient features. Meanwhile, a Gaussian mixture model is used to train and validate the system. To evaluate the proposed system, a dataset of tampered audios is created by mixing recordings from two different speakers. The performance of the proposed system in authenticating genuine audio is between 92.50% and 100%, and that in detecting forged audio is between 99.90 and 100%.
Original languageEnglish
Article number107122
Number of pages14
JournalComputers & Electrical Engineering
Volume91
Early online date21 Mar 2021
DOIs
Publication statusPublished - 1 May 2021

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