Deep Fake Audio Detection Using Machine Learning and Deep Learning
Baddam Rohith Reddy
, Kanchana
Deep Fake Audio Detection, Audio Spoof, Machine Learning, Convolutional Neural Network (CNN), Mel-frequency Cepstral Coefficients (MFCC), Voice Cloning, Deep Learning (DL).
With the rise of synthetic voice generation technologies, such as deepfakes and voice cloning, the potential for misuse in areas like fraud, misinformation, and identity theft has increased significantly. To combat this threat, Deep Learning has emerged as a powerful tool for detecting deep fake audio. Audio file features were extracted and visually presented using Mel Frequency Cepstral Coefficients (MFCC) and a Convolutional Neural Network (CNN)-based classification model to accurately differentiate between real and fake audio recordings. This paper provides a brief overview of deep fake audio detection using deep learning. It highlights the importance of this research and the potential applications of Deep Learning (DL) models in detecting and preventing the spread of deep fake audio.
"Deep Fake Audio Detection Using Machine Learning and Deep Learning", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.10, Issue 3, page no.a392-a395, March-2025, Available :https://ijsdr.org/papers/IJSDR2503047.pdf
Volume 10
Issue 3,
March-2025
Pages : a392-a395
Paper Reg. ID: IJSDR_300842
Published Paper Id: IJSDR2503047
Downloads: 00096
Research Area: Science and Technology
Country: Tambaram, Tamil Nadu, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
