Deepfake Detection

A Full-Stack Deepfake Detection Application developed FastAPI, Tensorflow, OpenCV, React.js and Tailwind CSS

To run this project locally:

Pre requisites: Python(>=3.10), Node.js(>=18.19)

• Clone this repository

git clone https://github.com/harshpx/deepfake-detection.git
cd deepfake-detection

• Setup python virtual environment and install dependencies

python3 -m venv ./venv
source ./venv/bin/activate
pip install -r requirements.txt

• Install frontend dependencies (present inside cilent directory)

cd client
npm install

• Go to parent directory (where app.py is located) and run the FastAPI server using:

uvicorn app:app --host 0.0.0.0 --port 8000 --reload

Now our FastAPI server must be running on localhost:8000 (make sure that your port 8000 is free before running previous command).

• Go to client directory and create another terminal (where package.json is located) and run the React App using:

npm run dev

Now our React App must be running on localhost:5173 (or on any port >5173 if it is not free).

Project description

The main model is a 10 Layer Deep CNN Architecture, which is optimized for effective image processing and classification, and specifically adapted to the deepfake detection task.

Model Structure

• With more than 653k parameters and dropout as the only regularization method, the model was quite straightforward.
• The dropout rate in dense layers is set at 0.5, preventing overfitting by forcing neurons to learn independently. 50% of neurons in each batch or training example are randomly removed, creating a new neural network for each batch, and the average prediction is obtained.
• The initial few Convolution layers had a kernel size of 5 and 64 filters, followed by max-pooling, unlike later layers with fewer filters and a dilation rate of 2.
• The benefit of dilated convolutions is obvious: they produce a bigger field of reception. The dilation rate was maintained at its standard level. You'll see that our network is only dilated in the final three layers.
• We use a binary classification model, and the output is based on sigmoid activation.
• Input image size used is [224,224,3].
• The training accuracy achieved was <99.9% and the validation accuracy too achieved was <99% (Extremely accurate).

Performance Analysis

• Training Logs

• Evaluation Metrics: Accuracy, Precision, Recall

• Confusion Matrix

  • True Positive: 602
  • False Positive: 7
  • False Negative: 5
  • True Negative: 586
  
  

• ROC-AUC curve

Post-Training Pipeline

Application Architecture (Client-Server)

Screenshots