Image Forgery Detection and Localization (and related) Papers List
Official code for CAT-Net: Compression Artifact Tracing Network. Image manipulation detection and localization.
Image forgery detection using convolutional neural networks. Group 10's final project for TU Delft's course CS4180 Deep Learning 2019.
Image Forgery Detection using Deep learning Graduation project
A collection of deep learning approaches and datasets publicly available for image forgery and deepfakes detection
This system is Used detect and highlight the image (Forgery) malpractices performed on modern-day digital images.
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.
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).
Passport document verifications using machine learning python sklearn
Fusion Transformer with Object Mask Guidance for Image Forgery Analysis
Official repository of "Deep Image Composition Meets Image Forgery"
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.
Image manipulation detection
Official repository of "Deep Image Restoration For Image Anti-Forensics"
Image forgery detection using PRNU approach.
Image Forgery Detection using ELA and Deep Learning
Image Forgery Detection using ELA and Deep Learning
Image forgery detection using CNN and Jgeg compression
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.
Add a description, image, and links to the image-forgery-detection topic page so that developers can more easily learn about it.
To associate your repository with the image-forgery-detection topic, visit your repo's landing page and select "manage topics."