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

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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
Short DOI https://doi.org/g84xxh
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