NYU DS-UA 301 Final Project
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Updated
Sep 2, 2024 - Jupyter Notebook
NYU DS-UA 301 Final Project
Academic group project undertaken as part of a class.
Employ deep learning and transfer learning techniques to classify images as "fake" or "real," ensuring authenticity preservation.
Fake Image Detection Using Machine Learning
Flask Web Interface to deploy ManTraNet and BusterNet for testing image manipulations
[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.
Resolves severe noise in the widely spread CASIA2.0 dataset ground-truth for Image Manipulation Detection
[ECCV 2022] TAFIM: Targeted Adversarial Attacks against Facial Image Manipulation
[NeurIPS'24 Spotlight] A comprehensive benchmark & codebase for Image manipulation detection/localization.
🏞 Steganography-based image integrity - Merkle tree nodes embedded into image chunks so that each chunk's integrity can be verified on its own.
Official code for CAT-Net: Compression Artifact Tracing Network. Image manipulation detection and localization.
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