| Name: | Andreas Rössler |
|---|---|
| Position: | Ph.D Candidate |
| E-Mail: | andreas.roessler@tum.de |
| Phone: | +49-89-289-19595 |
| Room No: | 02.13.035 |
Bio
I recieved my Bachelor's (2013) and Master's degree (2016) in Mathematics at the University of Regenbsurg before starting my career in computer vision. After an internship at the chair of Prof. Matthias Niessner, where I researched generative models, I am now a first year Ph.D. student with a focus on forgery detection as well the creation of tampered images and videos. Additionally, I am a tutor and guest lecturer for the class "Deep Learning for Computer Vision" at TUM.Research Interest
Forensics, Deep Learning with 4D shapes (in spatial and temporal domains), Reinforcement Learrning, (Conditional) Generative ModelsPublications
2019
| FaceForensics++: Learning to Detect Manipulated Facial Images |
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| Andreas Rössler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, Matthias Nießner |
| arXiv 2019 |
| In this paper, we examine the realism of state-of-the-art facial image manipulation methods, and how difficult it is to detect them - either automatically or by humans. In particular, we create a datasets that is focused on DeepFakes, Face2Face, and FaceSwap as prominent representatives for facial manipulations. |
| [video][code][bibtex][project page] |
2018
| ForensicTransfer: Weakly-supervised Domain Adaptation for Forgery Detection |
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| Davide Cozzolino, Justus Thies, Andreas Rössler, Christian Riess, Matthias Nießner, Luisa Verdoliva |
| arXiv 2018 |
| ForensicTransfer tackles two challenges in multimedia forensics. First, we devise a learning-based forensic detector which adapts well to new domains, i.e., novel manipulation methods. Second we handle scenarios where only a handful of fake examples are available during training. |
| [bibtex][project page] |
| FaceForensics: A Large-scale Video Dataset for Forgery Detection in Human Faces |
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| Andreas Rössler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, Matthias Nießner |
| arXiv 2018 |
| In this paper, we introduce FaceForensics, a large scale video dataset consisting of 1004 videos with more than 500000 frames, altered with Face2Face, that can be used for forgery detection and to train generative refinement methods. |
| [video][bibtex][project page] |