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001     1028993
005     20240722202104.0
024 7 _ |a 10.34734/FZJ-2024-04917
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037 _ _ |a FZJ-2024-04917
100 1 _ |a Pillath, Niclas
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245 _ _ |a Audio Deepfake Detection using the Stationary Wavelet Transform
|f - 2024-07-16
260 _ _ |c 2024
300 _ _ |a 76
336 7 _ |a Output Types/Supervised Student Publication
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502 _ _ |a Masterarbeit, Rheinische Friedrich-Wilhelms-Universität Bonn, 2024
|c Rheinische Friedrich-Wilhelms-Universität Bonn
|b Masterarbeit
|d 2024
|o 2024-07-16
520 _ _ |a 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.
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700 1 _ |a Suarez, Estela
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910 1 _ |a University of Bonn
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910 1 _ |a Forschungszentrum Jülich
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