Deepfake Detection in Call Recordings:A Deep Learning Solution for Voice Authentication

“Deepfake Detection in Call Recordings:A Deep Learning Solution for Voice Authentication”

Suhas Nimbalkar
Dept. of Computer Engineering,
Trinity College of Engineering & Research,Pune.
Pune,India.
suhasnimbalkar62@gmail.com

Sandip Bhande
Dept. of Computer Engineering,
Trinity College of Engineering & Research,Pune.
Pune,India.
saneepbhande12003@gmail.com

Niraj Bankar
Dept. of Computer Engineering,
Trinity College of Engineering & Research,Pune.
Pune,India.
nirajbankar3131@gmail.com

Dr. Geetika Narang
Dept. of Computer Engineering,
Trinity College of Engineering & Research,Pune.
Pune,India.
geetikanarang.tcoer@kjei.edu.in

Omkar Mali
Dept. of Computer Engineering,
Trinity College of Engineering & Research,Pune.
Pune,India.
omkarmali2103@gmail.com

Abstract— The emergence of deepfake technology has improved exponentially and this intensified the fears that surround the credibility of audio recordings, in instance telecommunication and security. This project proposes a full deep learning-based approach to deepfake voice recordings detection in call communications as an improvement to the voice authentication processes used. It is with this in mind that we came up with an adaptive architecture that positions convolutional neural networks (CNN) and recurrent neural network (RNN) in a way that assists in distinguishing between real and fabricated sounds.

The nature of the problem allows the use of a large amount of data collected from a wide variety of real and fake audio samples which serve for proper training and testing of the system. To improve the performance of the model, some strategies have been implemented including audio preprocessing such as spectrogram and features. This research adds to the existing body of literature on voice authentication but also seeks to underscore the need for solutions that secure audio communication in times when deepfakes are on the rise. Subsequent research will be dedicated to perfecting the existing model and assessing the feasibility of its use in practice.

Keywords— Deepfake Detection, Voice Authentication, Synthetic Speech Analysis, Speech Forensics, Audio Deepfake Identification, MFCCs, Spectrogram Analysis, CNNs, RNNs, Transformer Models, Automatic Speaker Verification (ASV), AI Security, Fraud Prevention, Adversarial Attacks, Machine Learning for Audio Forensics.