/SIH2024-Team22-404BrainNotFound

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Deepfake detection for Sih

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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 Documentation Under ReadMe.md

2. Directory Structure

For ease of understanding the project is structured in below format

SIH-2024
    |
    |--- 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

System Architecture

4. Run Project Locally

Step 1 : Clone this repo

Step 2 : Go to Django Application directory

Step 3 : Look out for ReadMe.md in Django Application Dir

Step 4 : Follow the instruction mentioned in the ReadMe

GoodLuck Figuring This Out

You can watch the demo vedio

https://drive.google.com/file/d/18YolvlOZjsoPgCFiWkVzV3HjfcU3ecLh/view?usp=sharing

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

6.1 Offline Contributors

  1. Mohak Gupta
  2. Aryyan Ahlawat
  3. Nancy
  4. Nishant Luheria
  5. Ananya
  6. Aastha

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