DEEP FAKE AUDIO DETECTION USING DEEP LEARNING

Abstract

The rapid advancement of artificial intelligence has led to the emergence of deepfake audio, where synthetic voices are
generated to mimic real individuals with high accuracy. While this technology has useful applications in entertainment and
accessibility, it also poses serious threats such as identity theft, misinformation, and fraud. This project, titled “Deep Fake
Audio Detection Using Deep Learning,” aims to develop an intelligent system capable of identifying whether an audio sample
is genuine or artificially generated. The proposed system leverages advanced deep learning techniques to analyze audio signals
and detect subtle patterns that distinguish real speech from synthetic audio. The methodology involves collecting a dataset of
real and deepfake audio samples, followed by preprocessing steps such as noise removal, normalization, and feature extraction.
Key audio features such as Mel-Frequency Cepstral Coefficients (MFCCs), spectrograms, and pitch variations are extracted to
capture both temporal and frequency characteristics of the signal. These features are then fed into deep learning models such as
Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), which are trained to classify audio samples
based on learned patterns. The system is optimized using techniques such as dropout, batch normalization, and hyperparameter
tuning to improve accuracy and generalization. Experimental results demonstrate that the proposed model achieves high
accuracy in detecting deepfake audio, outperforming traditional machine learning approaches. The system is capable of
identifying even subtle manipulations in speech, making it effective for real-world applications. This project contributes to
enhancing digital security by providing a reliable tool for detecting audio forgeries. It can be further extended to real-time
detection systems and integrated into media verification platforms. Overall, the proposed approach highlights the potential of
deep learning in combating emerging threats related to synthetic media.