xceptionnet

Official code for the paper "Adversarial Magnification to Deceive Deepfake Detection through Super Resolution"

Deep learning Papers Implementation using Tensorflow-keras

Dual-stream deep learning architecture for deepfake detection combining spatial (RGB) and frequency-domain (DCT) analysis with CBAM attention mechanisms.

DeepShield empowers individuals, journalists, and organizations to verify the authenticity of digital media in an age of misinformation.

The aim of this project is to implement/use pretrained popular architectures such as VGG16, VGG19, ResNet, AlexNet etc.

A deep learning-based web application for deepfake video detection, powered by the fine-tuned XceptionNet (Extreme Inception) model. The system allows users to upload videos for deepfake detection, processes them through the trained model, and provides results via a clean Django-based web interface.

The dataset has a collection of about 15000+ labeled images belonging to 67 categories. I am selecting only below 10 categories ⚰ airport_inside, auditorium, bakery, bathroom, bookstore, casino, church_inside, grocerystore, operating_room, warehouse Objective is to create a model that will able to classify images into these 10 categories.

Deepfake Detection System

Deep Fake detection training examples

Implementation and experimental evaluation of Xception-LSTM and Xception-GRU architectures for deepfake detection in journalism.

AI-powered system that detects deepfake and manipulated images and videos using pretrained models, temporal analysis, and confidence-based verification