Image Forgery Detection and Localization Using a Fully Convolutional Network

David Fischinger (Vortragende:r), David Schreiber, Martin Boyer

Publikation: Beitrag in Buch oder TagungsbandVortrag mit Beitrag in TagungsbandBegutachtung


To fight the growing problem of fake news – and specifically image manipulation – we propose a simple, yet efficient neural network architecture for detecting and localizing various image forgeries on a pixel-level. Robust features for forgery detection and localization were learned and the trained model performs well, even on heavily downscaled images, but without the excessive processing time of
competitive approaches based on image decomposition and merging of the fragmental results. We provide detailed explanations regarding the creation of our training dataset
comprising 1.9 million images. Finally, we compare the proposed solution against several state-of-the-art methods on four public benchmark datasets in order to demonstrate
its superior performance.
TitelProceedings of the OAGM Workshop 2022
UntertitelDigitalization for Smart Farming and Forestry
Redakteure/-innenHermann Bürstmayr, Andreas Gronauer, Andreas Holzinger, Peter M. Roth, Karl Stampfer
PublikationsstatusVeröffentlicht - 2023
VeranstaltungOAGM Workshop 2022 - Tulln, Österreich
Dauer: 7 Nov. 20228 Nov. 2022


WorkshopOAGM Workshop 2022

Research Field

  • Former Research Field - Surveillance and Protection


Untersuchen Sie die Forschungsthemen von „Image Forgery Detection and Localization Using a Fully Convolutional Network“. Zusammen bilden sie einen einzigartigen Fingerprint.

Diese Publikation zitieren