NYU DS-UA 301 Final Project
-
Updated
Sep 2, 2024 - Jupyter Notebook
NYU DS-UA 301 Final Project
Employ deep learning and transfer learning techniques to classify images as "fake" or "real," ensuring authenticity preservation.
Academic group project undertaken as part of a class.
Fake Image Detection Using Machine Learning
Resolves severe noise in the widely spread CASIA2.0 dataset ground-truth for Image Manipulation Detection
[AAAI 2022] MadisNet: Inharmonious Region Localization by Magnifying Domain Discrepancy
Corrections of resolution issue for common image manipulation localization datasets. (CASIA, Coverage, IMD2020)
[ICME2021]The first work on Deep Inharmonious Region Localization, which can help image harmonization in an adversarial way.
[ECCV 2022] TAFIM: Targeted Adversarial Attacks against Facial Image Manipulation
Flask Web Interface to deploy ManTraNet and BusterNet for testing image manipulations
🏞 Steganography-based image integrity - Merkle tree nodes embedded into image chunks so that each chunk's integrity can be verified on its own.
[NeurIPS'24 Spotlight] A comprehensive benchmark & codebase for Image manipulation detection/localization.
Official code for CAT-Net: Compression Artifact Tracing Network. Image manipulation detection and localization.
Add a description, image, and links to the image-manipulation-detection topic page so that developers can more easily learn about it.
To associate your repository with the image-manipulation-detection topic, visit your repo's landing page and select "manage topics."