DEEP FAKE AUDIO DETCTION USING DEEP LEARNING

Abstract

With the rapid advancement of artificial intelligence and deep learning technologies, the generation of synthetic audio, commonly known as deepfake audio, has become increasingly sophisticated and difficult to distinguish from real human speech. Deepfake audio poses significant threats in areas such as cybersecurity, digital forensics, misinformation, and identity fraud, as it can be used to impersonate individuals and manipulate information. This project focuses on the development of a deep learning-based system for detecting deepfake audio by analyzing speech patterns and identifying anomalies that differentiate synthetic audio from genuine recordings. The proposed system utilizes deep neural networks, including Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), to extract and learn complex features from audio signals such as MelFrequency Cepstral Coefficients (MFCCs), spectrograms, and temporal characteristics. The model is trained on a dataset containing both real and artificially generated audio samples to improve its ability to classify audio accurately. Feature extraction techniques play a crucial role in capturing subtle differences in frequency, pitch, and tone variations that are often overlooked by human perception. Experimental results demonstrate that the system achieves high accuracy in detecting deepfake audio, making it suitable for realworld applications such as voice authentication, fraud prevention, and media verification. However, challenges such as generalization to unseen data and evolving deepfake generation techniques remain significant. The proposed approach provides a reliable and scalable solution for combating audio-based deepfake threats and contributes to enhancing trust and security in digital communication systems.