Ghiro docker image

The aim of this project is to detect copy move forgery in image forgery detection. Based on Deep learning techniques(CNN, NN)

Semester VI project

Image Forgery Detection Using Passive Approach

Image forgery detection using CNN fusion model achieving 85% test accuracy. Features ELA preprocessing and fusion of InceptionV3, VGG16, and MobileNetV2. Ideal for digital forensics.

Final Year Project for C2001

Image forgery detection using CNN and Jgeg compression

Image forgery detection using PRNU approach.

Image Forgery Detection using ELA and Deep Learning

Image Forgery Detection using ELA and Deep Learning

Official repository of "Deep Image Restoration For Image Anti-Forensics"

Official repository of "Deep Image Composition Meets Image Forgery"

Image manipulation detection

This project focuses on detecting a specific form of image forgery known as a copy-move attack, in which a portion of an image is copied and pasted elsewhere.

Fusion Transformer with Object Mask Guidance for Image Forgery Analysis

Passport document verifications using machine learning python sklearn

Benford law helps in detecting the irregularity in a set of numbers. It can be used to detect fraud in image forensics(detecting whether the image is real or fake) or it can also be used to analyze inning scores of a cricketer(predicting whether that cricketer was involved in match-fixing or not).

IFAKE is an application for detecting image and video forgery, designed to help users verify the authenticity of digital media. This repository also contains the AI model and dataset that we developed for image tampering detection, providing an effective solution for detecting image and video manipulations.

Reproduced Code for Image Forgery Detection papers.

This system is Used detect and highlight the image (Forgery) malpractices performed on modern-day digital images.