DeepFake Detection for Face images

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

UNI subject on biometric systems with exercises and final project

an open-source framework for detecting audio generated from generative systems

208-Vietnamese-2D-Living-Face-Anti-Spoofing-Data

A repo for the Deepfake Detection Challenge on Kaggle

My submission for Srijan 22 Vol.12 , the annual departmental magazine of Department of Computer Science, University of Delhi.

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.

Here is the Asvspoof19 Laundered Dataset an deepfake audio website for downloading the data and preview the sample data

Detection & classification of deepfake

A Comparative study on the performance of multiple transfer learning model in deepfake detection

CyberSight is our final project at Bar-Ilan University. The primary objective of the project is to identify whether a given picture of a human face is generated by artificial intelligence or depicts a real person.

A Full-Stack Deepfake Detection App, developed using TensorFlow, FastAPI and React.js

This repository employs TensorFlow-Keras and MTCNN (Multi-Task Cascaded Convolutional Networks) to build an efficient deepfake detection system.

🐱‍👤 [Kaggle] Real/Fake image classification

DeepFake

Compilation of the Deepfake Detection Datasets

This project presents Deepfake identification using machine learning techniques to detect synthetic images. Machine learning plays a significant role in assisting with complicated and convoluted problems that breaches human ability. The precision and consistency with which technology can reliably and easily discern the integrity of digitalized v…

An exploration study to understand the research landscape for Deep Fakes.