Detection of Human Edited Images using CNN, VGG16, Xception, ELA, Ensemble Learning.

aim of this project is to give insight into authenticity of an image using ELA and metadata analysis based weather validation

Multi-feature Forgery Detection Deep-Learning based Framework

Employing Error Level Analysis (ELA) and Edge Detection techniques, this project aims to identify potential image forgery by analyzing discrepancies in error levels and abrupt intensity changes within images.

Image Forgery Detection using ELA and Deep Learning

Image Tampering Detection using ELA and CNN

Academic group project undertaken as part of a class.

Simple Tampered Image Detection using Error Level Analysis and Convolutional Neural Network with Flask

Image Forgery Detection using ELA and Deep Learning

Image Tampering Detection WebApp made with Flask

Python CLI tool to visually detect photoshopped pictures using Error Level Analysis

A program for my undergraduate thesis in Computer Science, Universitas Pendidikan Indonesia (Indonesia University of Education).

Edited Images Analyser

Separates real and fake images

Classifies a given image as authentic or tampered by doing two levels of analysis. Implemented using PyTorch.

Python implementation of the Error Level Analysis algorithm in scikit-image with a GUI made in TKinter

Detects the authenticity of an image using Error Level Analysis and Convolutional Neural Networks.

Classifies a given aadhaar image to real or fake by doing two levels of analysis.

This tool compares the original image to a recompressed version. This can make manipulated regions stand out in various ways. For example they can be darker or brighter than similar regions which have not been manipulated.