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Resilience of Biometric Authentication of Voice Assistants against Deepfakes
Šandor, Oskar ; Firc, Anton (oponent) ; Malinka, Kamil (vedoucí práce)
With the rise of deepfake technology, imitating the voice of strangers has become a lot easier. It is no longer necessary to have a professional impersonator to imitate the voice of a person to possibly deceive a human or machine. Attackers only need a few recordings of a person's voice, regardless of the content, to create a voice clone using online or open-source tools. In that case, he or she can create recordings with content that the person may have never said. These recordings can be misused, for example, for unauthorized use of voice-assistant devices. The aim of this work is to determine whether voice assistants can recognize synthetized recordings (deepfakes). Experiments conducted in this thesis show that deepfakes created in a matter of minutes can spoof speaker recognition in voice assistants and can be used to carry out several attacks.

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