Deepfake Audio Detection Project

Overview

This project was developed during the AIAmplify Hackathon, a 24-hour hackathon focused on using AI to address real-world challenges. The goal of this project is to detect deepfake audio using machine learning techniques. The project uses MFCC (Mel-frequency cepstral coefficients) features extracted from audio files and a Support Vector Machine (SVM) classifier to differentiate between genuine and deepfake audio.

Contributors

• Noor Chauhan
• Abhishek Khadgi
• Omkar Sapkal
• Himanshi Shinde
• Furqan Ali

Table of Contents

1. Introduction
2. Overview
3. Contributors
4. Installation
5. How to Use
   • Training the Model
   • Analyzing Audio
6. License

Installation

To initialize the project, follow these steps:

1. Clone the repository to your local machine:

   git clone https://github.com/your-username/deepfake-audio-detection.git
   cd deepfake-audio-detection
   

2. Set up a virtual environment (optional but recommended):

   # For Windows
   python -m venv venv
   venv\Scripts\activate
   
   # For Linux/macOS
   python3 -m venv venv
   source venv/bin/activate
   

3. Install the required dependencies

How to Use Training the Model

To train the SVM model with the provided data, follow these steps:

1. Prepare the dataset: Place genuine audio files in the real_audio directory and deepfake audio files in the deepfake_audio directory.

2. Run the training script:

   python main.py
   

   After sucessfully running the main script, it will initially ask you to provide the path of the voice to analyze, provide it with the path and the

3. Run the web app by:

   python app.py
   

   The training script will extract MFCC features from the audio files, split the data into training and testing sets, scale the features, train the SVM model, and save the trained model and scaler for future use.

Analyzing Audio

To classify an audio file as genuine or deepfake, follow these steps:

1. Ensure the trained model and scaler are available (already saved during training).

2. Run the analysis script:

   python analyze_audio.py path/to/your/audio/file.wav
   

   Replace path/to/your/audio/file.wav with the path to the audio file you want to analyze. The script will extract MFCC features from the audio, scale the features using the saved scaler, pass the features to the trained SVM model, and display the classification result.

Contribution & License

• For contributing, fork this project and compare and submit a pull request with proper description to your changed/added features
• OpenSource MIT License, for more information read the License.