International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 8, Issue 3 (May-June 2026) Submit your research before last 3 days of June to publish your research paper in the issue of May-June.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 8 Issue 3
May-June 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Deepfake Audio Detection using MFCC Features

Author(s) Priya N V, Pavan H, Prajwal S, Varun R, Vinay A
Country India
Abstract The proliferation of deepfake audio technologies poses significant threats to privacy and security, as AI-generated voices can convincingly mimic real human speech, leading to potential misuse in identity theft, fraud, and misinformation campaigns. This project aims to develop a robust system for detecting deepfake audio by leveraging advanced machine learning algorithms and signal processing techniques. The system will extract key features from audio recordings, such as Mel-Frequency Cepstral Coefficients (MFCCs), and utilize a Random Forest classifier to differentiate between genuine and manipulated audio. By analyzing subtle inconsistencies in the audio signals, the system can accurately identify deepfake content, thereby enhancing the integrity of digital communications.
Keywords The rise of deepfake audio technologies poses significant threats to privacy and security, as AI-generated voices can convincingly mimic real human speech, leading to potential misuse in identity theft, fraud, and misinformation campaigns. This project aims to develop a robust system for detecting deepfake audio by leveraging advanced machine learning algorithms and signal processing techniques. The system will extract key features from audio recordings, such as Mel-Frequency Cepstral Coefficients (MFCCs), and utilize a Random Forest classifier to differentiate between genuine and manipulated audio.
Field Engineering
Published In Volume 7, Issue 1, January-February 2025
Published On 2025-02-12
DOI https://doi.org/10.36948/ijfmr.2025.v07i01.36558
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 8 Isu 3Cover Page Vol 8 Isu 2Cover Page Vol 8 Isu 2

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijfmr.com
Powered by Sky Research Publication and Journals