Detectify is a deep learning system that detects AI-generated fake videos (deepfakes) using CNN and LSTM-based RNNs. Trained on datasets like Face-Forensic++, Deepfake Detection Challenge, and Celeb-DF, Detectify offers real-time video manipulation detection to combat misinformation and misuse of deepfake technology.

This repository performs audio cloning, text extraction, and NLP tasks using LLMs, ML, and AI models.

3D Passive Face Liveness Detection (Anti-Spoofing) & Deepfake detection. A single image is needed to compute liveness score. 99,67% accuracy on our dataset and perfect scores on multiple public datasets (NUAA, CASIA FASD, MSU...).

Weakly supervised deepfake localization

Code for Video Deepfake Detector from "MINTIME: Multi-Identity Size-Invariant Video Deepfake Detection", paper available on IEEE Transactions on Information Forensics and Security.

This repository is related to our Dataset and Detection code from the paper: AI-Synthesized Voice Detection Using Neural Vocoder Artifacts accepted in CVPR Workshop on Media Forensic 2023.

NeuroMelNet - End-to-End binary classification model for AI speech detection with PyTorch implementation. Python API & Tg-bot API

Artificial Intelligence model trained with a CNN to detect fake images generated with AI using Deep learning.

A Survey on Deepfake Generation and Detection

Machine Learining models for Video and Audio Analysis using Deep Learning for Deepfake Detection.

Code, model and data for our paper: K. Tsigos, E. Apostolidis, S. Baxevanakis, S. Papadopoulos, V. Mezaris, "Towards Quantitative Evaluation of Explainable AI Methods for Deepfake Detection", Proc. ACM Int. Workshop on Multimedia AI against Disinformation (MAD’24) at the ACM Int. Conf. on Multimedia Retrieval (ICMR’24), Thailand, June 2024.

Deepfake Detection using a Dual Stream Network with Interpretable Deep Learning

Detection & classification of deepfake

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

A Gradio-based web application that detects whether an image is a deepfake. The application uses a pre-trained InceptionResnetV1 model from the facenet_pytorch library for face recognition, and pytorch-grad-cam for visual explainability.

Detecting Deepfake Human Face Images Using Transfer Learning: A Comparative Study

Code and pre-trained models for our paper "CLIPping the Deception: Adapting Vision-Language Models for Universal Deepfake Detection".

This repository contains the codes of "Lips Are Lying: Spotting the Temporal Inconsistency between Audio and Visual in Lip-syncing DeepFakes".

Custom DenseNet for deepfake detection

A War Beyond Deepfake: Benchmarking Facial Counterfeits and Countermeasures