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@MASTERSTHESIS{Pillath:1028993,
      author       = {Pillath, Niclas},
      othercontributors = {Suarez, Estela},
      title        = {{A}udio {D}eepfake {D}etection using the {S}tationary
                      {W}avelet {T}ransform},
      school       = {Rheinische Friedrich-Wilhelms-Universität Bonn},
      type         = {Masterarbeit},
      reportid     = {FZJ-2024-04917},
      pages        = {76},
      year         = {2024},
      note         = {Masterarbeit, Rheinische Friedrich-Wilhelms-Universität
                      Bonn, 2024},
      abstract     = {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.},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / 5122 - Future Computing
                      $\&$ Big Data Systems (POF4-512)},
      pid          = {G:(DE-HGF)POF4-5112 / G:(DE-HGF)POF4-5122},
      typ          = {PUB:(DE-HGF)19},
      doi          = {10.34734/FZJ-2024-04917},
      url          = {https://juser.fz-juelich.de/record/1028993},
}