TY  - THES
AU  - Pillath, Niclas
TI  - Audio Deepfake Detection using the Stationary Wavelet Transform
PB  - Rheinische Friedrich-Wilhelms-Universität Bonn
VL  - Masterarbeit
M1  - FZJ-2024-04917
SP  - 76
PY  - 2024
N1  - Masterarbeit, Rheinische Friedrich-Wilhelms-Universität Bonn, 2024
AB  - Recent advances in generative modelling have uncovered new avenues for creative expression, while also raising the potential of malicious misuse. Deepfakes pose significant risks to personal privacy, information integrity and cybersecurity. One possible countermeasure against deepfakes are robust detection models based on deep learning. In the context of audio, convolutional architectures have demonstrated good recognition rates. The raw audio data is usually transformed into a suitable format for convolutional neural networks. Current state-of-the art models employ Fourier or wavelet transforms. This thesis investigates the detection of audio fakes using deep convolutional networks that process stationary wavelet transform inputs. The results show functional detector models, but also reveal limitations of the stationary wavelet transform.
LB  - PUB:(DE-HGF)19
DO  - DOI:10.34734/FZJ-2024-04917
UR  - https://juser.fz-juelich.de/record/1028993
ER  -