Image Tampering Detection using ELA and CNN
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Updated
Jun 28, 2023 - Jupyter Notebook
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.
Edited Images Analyser
Designed an ANN-based system which will identify and classify the morphed images By processing through ELA
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.
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
Image Forgery Detection using ELA and Deep Learning
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.
Simple Tampered Image Detection using Error Level Analysis and Convolutional Neural Network with Flask
Separates real and fake images
Image Tampering Detection WebApp made with Flask
Python implementation of the Error Level Analysis algorithm in scikit-image with a GUI made in TKinter
Image Forgery Detection using ELA and Deep Learning
Academic group project undertaken as part of a class.
Multi-feature Forgery Detection Deep-Learning based Framework
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