Results

Fooling State-of-the-Art Deepfake Detection with High-Quality Deepfakes

Left: Forward pass of our model. The loss is computed separately for each identity (A and B) and finally summed up.
Right: Detailed architecture of the proposed decoder.

This paper emphasizes the need for robust deepfake detectors in the face of increasing security and privacy concerns. We propose a novel autoencoder and face blending technique to generate high-quality deepfakes, which we use to fool a State-of-the-Art deepfake detector. The results highlight the importance of including high-quality fakes in the training datasets of deepfake detectors for improved generalization and detection of manipulations in real-world scenarios.

Key publication

Beckmann, A., Hilsmann, A., Eisert, P. (2023).
Fooling State-of-the-Art Deepfake Detection with High-Quality Deepfakes. arXiv.
Source

Decoding subjective emotional arousal from EEG during an immersive virtual reality experience

Schematic of the experimental setup and analysis approach.

We successfully decoded self-reported emotional arousal during an immersive VR experience involving virtual rollercoasters from EEG-derived parieto-occipital alpha power (Hofmann, Klotzsche, Mariola et al., 2021).

Key publication

Hofmann, S. M., Klotzsche, F., Mariola, A., Nikulin, V., Villringer, A., & Gaebler, M. (2021).
Decoding subjective emotional arousal from EEG during an immersive virtual reality experience. eLife.
Source press release | eLife digest | twitter thread