Deepfake Audio Detection using MFCC Features

The proliferation of deepfake audio technologies poses significant threats to privacy and security, as AI-generated voices can convincingly mimic real human speech, leading to potential misuse in identity theft, fraud, and misinformation campaigns. This project aims to develop a robust system for detecting deepfake audio by leveraging advanced machine learning algorithms and signal processing techniques. The system will extract key features from audio recordings, such as Mel-Frequency Cepstral Coefficients (MFCCs), and utilize a Random Forest classifier to differentiate between genuine and manipulated audio. By analyzing subtle inconsistencies in the audio signals, the system can accurately identify deepfake content, thereby enhancing the integrity of digital communications.