Image Forgery Detection and Localization (and related) Papers List
Image Tampering Detection using ELA and CNN
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.
aim of this project is to give insight into authenticity of an image using ELA and metadata analysis based weather validation
This repository was built for image tampering detection.
AI-powered image forensics tool that detects deepfakes and image tampering, then localizes forged regions using ELA + ResNet50.
PixelGuard is a powerful Python-based tool designed to detect image tampering using SHA-256 hashing, metadata analysis, and pixel-level comparison. It helps verify the authenticity of images and identify unauthorized modifications, making it useful for digital forensics, cybersecurity, and content verification.
Image Tampering Detection WebApp made with Flask
Paper code: Image tampering localization network based on multi-class attention and progressive subtraction
AI-powered digital image authenticity verification platform using CNN, Random Forest and Error Level Analysis (ELA).
MATLAB-based image tampering detection using noise feature extraction, high-pass filtering, statistical analysis, and block-wise noise inconsistency analysis.
A privacy-preserving image forgery detection framework using Federated Learning. Combines FedYogi optimization with CNN and ResNet50 to handle non-IID forensic data. Trains models collaboratively without sharing raw images, improving stability and accuracy on CASIA 1.0.
AI-powered document tampering detector. Spots edited receipts, forged certificates, and AI-generated fakes in under 35ms using multi-domain forensics on GPU.
Integrated image forensics framework: classic splicing/tampering detection (Java + MATLAB) extended with Python AI-generated image detection, unified AI/edited/original classification, and a commercial viability report.
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