Towards deepfake detection that actually works

Code for Video Deepfake Detection model from "Combining EfficientNet and Vision Transformers for Video Deepfake Detection" presented at ICIAP 2021.

Determine whether a given video sequence has been manipulated or synthetically generated

Deepfakes Video classification via CNN, LSTM, C3D and triplets [IWBF'20]

Unofficial Implementation: Learning Self-Consistency for Deepfake Detection

This repository contains our POC for a website which can easily check videos for manipulated areas. It was part of the Hackathon for Good in the Hague, 2019.

Deepfake faces detection from forged videos where used explainable AI for models' robustness as well as cost sensitive methods for mitigating dataset imbalance problem

This project was completed as part of the Deep Learning course (GLO-4030) at Laval University. Its goal is to detect deepfake videos using deep learning techniques on the FaceForensics dataset. We were able to achieve deepfake detection by using the EfficientNet model.

The purpose of this project is to develop an AI-powered system capable of detecting deepfake facial data in biometric systems. By leveraging machine learning, specifically XceptionNet architecture, the project aims to classify facial data as real or fake with high accuracy and reliability.

Reproduction/refactoring of the FaceForensics++ classification process.

A Deepfake Detection Project using EfficientNetV2 and FaceForensics++ with Gradio as UI

This project is based on the paper Representative Forgery Mining for Deep Fake Detection.

This repo is an enhanced toolkit with some updated methods processing original FaceForensics++ dataset.

Replication code for the paper 'Towards DeepFake video forensics based on facial textural disparities in multi-color channels'

DeepFakeNet leverages advanced AI models like DeeperMesoInception4 for reliable detection of deepfake videos, analyzing FaceForensics++ dataset to ensure digital trust.