Image Tampering Detection using ELA and CNN

ELA 全称：Error Level Analysis ，汉译为“错误级别分析”或者叫“误差分析”。通过检测特定压缩比率重新绘制图像后造成的误差分布，可用于识别JPEG图像的压缩。

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

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

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

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

Edited Images Analyser

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.

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.

Image Forgery Detection using ELA and Deep Learning

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

Image Forgery Detection using ELA and Deep Learning

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

Separates real and fake images

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

Image Tampering Detection WebApp made with Flask

Multi-feature Forgery Detection Deep-Learning based Framework

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

Academic group project undertaken as part of a class.