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

A state-of-the-art, open-source deepfake detection system built with PyTorch and EfficientNet-B0, featuring a user-friendly web interface for real-time image and video analysis.

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

An Image Analyzer that can analyze and give Real or Fake predictions with confidence scores

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.

Repository for replicating the experiments described in the articles developed as part of the joint research between Universitas Mercatorum and Universidad de Cádiz (UCA).

Deepfake Detection System using CV +DL

Deepfake detection on FaceForensics++ using ResNet50, Xception(+SE), and Swin(+SE) with face crops vs full frames, frame sampling, aggregation, and Grad-CAM.

Reproduction/refactoring of the FaceForensics++ classification process.

State-of-the-art forensic toolkit for deepfake detection. Features a full-network fine-tuned Xception model (98.5% accuracy) on FaceForensics++ and a Glassmorphic web UI.

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

Frequency-aware deepfake detection under lossy H.264 compression using XceptionNet, DCT features, learnable frequency masking, and cross-attention fusion.

Detect AI-generated face manipulations in real time — with visual explanations showing exactly where the model found tampering. Supports images, videos, and live webcam. Powered by EfficientNet-B4 and Grad-CAM XAI.

An explainable deepfake detection framework leveraging Vision Transformers (ViT), temporal pooling, and Grad-CAM for interpretable image and video forgery detection across benchmark datasets.

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.