suraj1849 / deepfake-video-detection

This projects aims in detection of video deepfakes using deep learning techniques like RestNext and LSTM. We have achived deepfake detection by using transfer learning where the pretrained RestNext CNN is used to obtain a feature vector, further the LSTM layer is trained using the features.

DeepFake-Video-Detection

Deepfake detection using Deep Learning (ResNext and LSTM)

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1. Introduction

This projects aims in detection of video deepfakes using deep learning techniques like ResNext and LSTM. We have achived deepfake detection by using transfer learning where the pretrained ResNext CNN is used to obtain a feature vector, further the LSTM layer is trained using the features.

2. Directory Structure

For ease of understanding the project is structured in below format

Deepfake_detection_using_deep_learning
    |
    |--- Django Application
    |--- Model Creation

1. Django Application
   • This directory consists of the django made application of our work. Where a user can upload the video and submit it to the model for prediction. The trained model performs the prediction and the result is displayed on the screen.
2. Model Creation
   • This directory consists of the step by step process of creating and training a deepfake detection model using our approach.

3. Our Results

Model Name	No of videos	No of Frames	Accuracy
model_84_acc_10_frames_final_data.pt	6000	10	84.21461
model_87_acc_20_frames_final_data.pt	6000	20	87.79160
model_89_acc_40_frames_final_data.pt	6000	40	89.34681
model_90_acc_60_frames_final_data.pt	6000	60	90.59097
model_91_acc_80_frames_final_data.pt	6000	80	91.49818
model_93_acc_100_frames_final_data.pt	6000	100	93.58794

4. We welcome Open Source Contribution.

Below are the some changes that can be applied to the project. New Ideas will be appreciated.

• Deploying the applications in free cloud
• Creating open source API for detection
• Batch processing of entire video instead of processing first 'x' frames.
• Optimizing the code for faster execution.