Leslie Wöhler

Leslie Wöhler
Technische Universität Braunschweig


How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
Citations since 2016
5 Research Items
11 Citations


Publications (5)
We present a novel framework for the evaluation of eye tracking data in portrait videos including the automatic generation of customized areas of interest (AOIs) based on facial landmarks. In contrast to previous work, our framework allows the user to flexibly create AOIs by grouping the detected landmarks. Moreover, their shape and size can be mod...
Conference Paper
Full-text available
In this paper, we report on perceptual experiments indicating that there are distinct and quantitatively measurable differences in the way we visually perceive genuine versus face-swapped videos. Recent progress in deep learning has made face-swapping techniques a powerful tool for creative purposes, but also a means for unethical forgeries. Curren...
Full-text available
Videos obtained by current face swapping techniques can contain artifacts potentially detectable, yet unobtrusive to human observers. However, the perceptual differences between real and altered videos, as well as properties leading humans to classify a video as manipulated, are still unclear. Thus, to support the research on perceived realism and...


Cited By


Project (1)
Recent advances in deep learning-based techniques enable highly realistic facial video manipulations. We investigate the response of human observers’ on these manipulated videos in order to assess the perceived realness of modified faces and their conveyed emotions. Facial reenactment and face swapping offer great possibilities in creative fields like the post-processing of movie materials. However, they can also easily be abused to create defamatory video content in order to hurt the reputation of the target. As humans are highly specialized in processing and analyzing faces, we aim to investigate perception towards current facial manipulation techniques. Our insights can guide both the creation of virtual actors with a high perceived realness as well as the detection of manipulations based on explicit and implicit feedback of observers.