Combating disinformation by equipping journalists with new image manipulation insights and detection methods
Niels Van Kets
As project manager at IDLab-MEDIA, I gap the bridge between academics and industry in order to deliver impactful, industry driven technological innovations. I am specialized in collaborative and interdisciplinary research projects within the TME (Telecommunication, Media and Entertainment) vertical. I lead the drafting process of such projects and follow up the execution once started.
Furthermore, I assist my colleagues in defining and setting up high-end, highly reliable system architectures and best practices based on their R&D needs.
Lastly, I play a key role in defining and steering overarching initiatives within the Art and Science Interaction Lab - a unique, highly flexible and modular “interaction science” research facility. I also managed this modular infrastructure from a systems architect point of view.
From 2013 until 2018 I was involved in several collaborative research projects as research engineer within the TME vertical. I acted as DevOps on several innovative solutions (TRL <= 6), focusing on full stack development and scalable system architectures.
We are happy to announce that our interdisciplinary paper titled "Linking the cognitive load induced by route instruction types and building configuration during indoor route guidance, a usability study in VR" was accepted for publication in the International Journal of Geographical Information Science.
Our paper titled "Fast Fallback Watermark Detection using Perceptual Hashes" was accepted for publication to the special issue "Recent Developments and Applications of Image Watermarking" of MDPI Electronics
Funding granted for Interdisciplinary Research Project on computer vision and intelligent mowing patterns to automate biodiversity management in grasslands
Funding granted for Interdisciplinary Research Project on improving musical group experiences in VR, using a biofeedback control system
The RHETORiC project will therefore design interface elements and machine learning approaches to create semi-automated tools for news editors and media audiences to identify and counter polarization and contribute constuctively to online debates.
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