Identify deepfakes through original and modified XceptionNet models. Feed face images to the network and receive predictions whether the face is original/real, or the result of deepfake techniques. Built upon tensorflow and opencv-python.

It is recommended to use conda for this project. Below are the script to replicate the project environment.

Steps:

1. Create conda environment. below, the env is named torchit.
2. Activate the conda env
3. Install dlib through conda-forge channel. DO NOT use pip for dlib as it requires numerous cublas, cudnn and cuda-related libraries.
4. Install other packages through pip

conda create -n torchit python --yes
conda activate torchit
conda install -c conda-forge dlib --yes
pip install torch torchvision facenet-pytorch opencv-python 

Install and run jupyterlab (optionally) through pip as well with:

pip install jupyterlab jupyterlab-lsp  # Install jupyterlab and LSP (language server protocol) to enable documentation, and error checkings
jupyter-lab  # run jupyterlab

All codes (python, notebook) are available inside src/.

This python script and its notebooks are licensed under MIT License.