This repository contains methods used for deepfakes detection that utilizes 3D biometric characteristics extracted from the face. In order not to use original 3D data, 3DMMs were used. In the presented methods temporal features were taken into account. To achieve this, the frames of the videos that are used on the methods are firstly converted into parameters that express the 3DMMs. Features that help identify deepfakes are also extracted from facial landmarks. Such features hold information about how open or close the eyes and the mouth are, as well as specific angles of the face.

The methods are divided into two categories, One Class Classifiers and Binary Classifiers.The first case includes VAEs and Gans,two for each category, and the second case a deep convolutional network , EfficientNetv2. It was observed that GANs show relatively good results in recognizing deepfakes, results that outperform those of VAE. However, the Binary Classifier presented the best results. Also, the features proposed in this work improved the performance of the Binary Classifier and are, therefore, considered capable of helping to identify deepfakes.

Two Datasets were used to test and train the models, VoxCeleb2 and FaceForensics++. The One Class Classifier models were trained on VoxCeleb2 and tested on FaceForesics++ while the EfficientNet was trained using both datasets.

The One Class Classifier models that are implemented are:

• Variational Autoencoder implemented using a Dense architecture and thus named DenseVAE
• VQVAE
• GAN using convolutional layers
• OCGAN

The architecture of each model and the training method that were used are described in detail in the provided deepfakes_detection_thesis.pdf file.

To transform the input videos into the features that are used to train the models, the steps described in Dataset_Preprocessing folder should be followed. The One_Class_Classifiers folder includes the 4 One Class Classifier models alongside the scripts used for their training and testing. File test_trained_one_class_classifier_models.py can be used to test all One Class Classifier models. Moreover, Binary_Classifier folder includes the EfficientNetV2 model and the training/testing scripts.

The provided code is part of my Thesis Detecting deepfakes by imprinting face’s 3D biometric characteristics conducted during my final year studying at the Electrical & Computer Engineering School of Aristotle University of Thessaloniki.