Steps:

• Grab the required packages from requirements.txt using pip

prepare and train Dataset:

• We have used the Kaggle Deepfake Challange dataset, Link: https://www.kaggle.com/c/deepfake-detection-challenge/data

• Download the dataset and from train_sample_videos folder, extract faces from the videos. Put them in the corresponding folders inside dataset. Classes are predefined already.

• Run train_dataset.py to train and generate models.

python3.py train_dataset.py dataset/ classes.txt  result/

Predefined settings:

Epoch: 10 / 30 [First/Final stage]
Learning rate: 5e-3 / 5e-4
Batch size: 32 / 64

• Then take the best model from examining the graph and run app.py to detect videos. It can take a video file or a youtube-dl supported video link as a input. Note that we've tested online links only with Youtube so your results may vary.

Note:

There's also a basic image predictor which takes a LOT less time compared to a video. Use image_prediction.py

python3 image_prediction.py path_to_model.p classes.txt input_image.jpg

Credits:

Francois Chollet
Xception: Deep Learning with Depthwise Separable Convolutions
https://arxiv.org/pdf/1610.02357.pdf