Abstract
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.
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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the OAGM Workshop 2022 |
| Subtitle of host publication | Digitalization for Smart Farming and Forestry |
| Editors | Hermann Bürstmayr, Andreas Gronauer, Andreas Holzinger, Peter M. Roth, Karl Stampfer |
| Pages | 19-25 |
| Number of pages | 7 |
| Publication status | Published - 2023 |
| Event | OAGM Workshop 2022 - Tulln, Austria Duration: 7 Nov 2022 → 8 Nov 2022 |
Workshop
| Workshop | OAGM Workshop 2022 |
|---|---|
| Country/Territory | Austria |
| Period | 7/11/22 → 8/11/22 |
Research Field
- Former Research Field - Surveillance and Protection