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001028993 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-04917
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001028993 1001_ $$0P:(DE-HGF)0$$aPillath, Niclas$$b0$$eCorresponding author
001028993 245__ $$aAudio Deepfake Detection using the Stationary Wavelet Transform$$f - 2024-07-16
001028993 260__ $$c2024
001028993 300__ $$a76
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001028993 502__ $$aMasterarbeit, Rheinische Friedrich-Wilhelms-Universität Bonn, 2024$$bMasterarbeit$$cRheinische Friedrich-Wilhelms-Universität Bonn$$d2024$$o2024-07-16
001028993 520__ $$aRecent 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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001028993 7001_ $$0P:(DE-Juel1)142361$$aSuarez, Estela$$b1$$eReviewer$$ufzj
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001028993 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$aUniversity of Bonn$$b0
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001028993 9131_ $$0G:(DE-HGF)POF4-512$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5122$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vSupercomputing & Big Data Infrastructures$$x1
001028993 9141_ $$y2024
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