Deepfake detection using Deep Learning (ResNext and LSTM)

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Latest Update

We have dockerised the Django Application now you can spin up a container within seconds without worring about dependencies

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. For more details follow the documentaion.

You can also watch this Youtube video to get a better intuition about the project. You can watch this playList for step by step installation.

You can read more about the project on  

2. Directory Structure

For ease of understanding the project is structured in below format

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

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. Documentation
   • This directory consists of all the documentation done during the project

3. System Architecture

4. Demo

You can watch the youtube video for demo

5. 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

6. Contributors

1. Abhijit Jadhav
2. Jay Patel
3. Hitendra Patil
4. Abhishek Patange

7. License

8. 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.

Completed

• Dockerizing the app
• Enabling working of project on Non Cuda Computers. i.e on normal or AMD GPUs : not possible as dlib uses CUDA internally

9. Dont forget to Star⭐ this repo 😉 and