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

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 we developed an adaptive architecture which arrange convolutional neural networks (CNN) and recurrent neural network (RNN) in a manner that helps in discerning between real and fake 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.

Introduction

The rise of deepfake technology, particularly in audio, poses significant security threats by enabling highly realistic synthetic voices that can bypass voice authentication systems used in telecommunications, banking, and security. This has driven research into deepfake audio detection using advanced machine learning methods.

The project described aims to develop a deepfake voice detection system based on deep learning, specifically combining Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to capture both spatial and temporal audio features. The system preprocesses audio data, extracts critical features like Mel-Frequency Cepstral Coefficients (MFCCs) and spectrograms, and trains a hybrid CNN-RNN model to distinguish between real and manipulated audio. Performance metrics such as accuracy and precision evaluate the model, with a user-friendly front end to facilitate real-time detection.

The literature review highlights various studies on deepfake audio datasets, detection techniques using deep learning, explainable AI integration, and the socio-ethical impacts of deepfakes. The methodology emphasizes extensive data collection, preprocessing, feature extraction, and training using hybrid neural network architectures. The proposed system involves multiple stages, including input data processing, feature selection, deep learning model training, and real-time classification, aiming to enhance security and trust in voice-based communications.

Conclusion

Indeed, over the years, animosity towards audio communications, especially voice-based authentication, has taken a new turn thanks to the recent advent of deepfake technology. This project has also focused on deepfake audio, and how they present a more serious challenge to impersonation than what is already encountered, and the problems of the existing solutions, which justifies the need for fresh ideas to deal with them. Using advanced deep learning neural framework like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), the research focuses on developing a system aimed at call recordings dubbed deepfake. Extensive tests and performance evaluations of the said systems incorporated in call records showed high accuracy in differentiating between real and fake audios suggesting a leap from the conventional approach. The results from this study are ground breaking and are expected to provide a broader scope in audio forensics and deepfake detection in the near future, as well as support services for agencies whose operations operate on voice authentication like telecommunications, banking and security. Thus, appropriate measures implemented to detect effective cloaking techniques will assist institutions to better contain such activities as fraud and loss of confidential data. All in all, this project addresses in greater detail the challenge of deepfake audio in voice authentication systems. With the adoption of deep learning, audio communication systems would not only be more secured but also more dependable, which in turn will ease the fear associated with the use of voice operated systems and contribute significantly to the need to implement systems that will counteract emerging deepfake challenges.

References

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Copyright

Copyright © 2025 Mr. Suhas Hanumant Nimbalkar , Mr. Niraj Sunil Bankar, Mr. Sandip Dadaso Bhande , Mr. Omkar Vishwanath Mali, Dr. Geetika Narang . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.