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@inproceedings{2213202, author = {Firc, Anton and Malinka, Kamil}, booktitle = {SAC '22: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing}, doi = {http://dx.doi.org/10.1145/3477314.3507013}, keywords = {deepfakes, speech verification, voice biometrics, machine learning, cybersecurity}, howpublished = {elektronická verze "online"}, pages = {1646--1655}, publisher = {Association for Computing Machinery}, title = {The dawn of a text-dependent society: deepfakes as a threat to speech verification systems}, year = {2022} }
TY - JOUR ID - 2213202 AU - Firc, Anton - Malinka, Kamil PY - 2022 TI - The dawn of a text-dependent society: deepfakes as a threat to speech verification systems PB - Association for Computing Machinery KW - deepfakes, speech verification, voice biometrics, machine learning, cybersecurity N2 - We are already aware that deepfakes pose threats to humankind. Nowadays, mostly as fake news or disinformation; however, there are still unexplored areas such as using deepfakes to spoof voice verification. We present a real-world use case for spoofing voice authentication in a customer care call center. Based on this scenario, we evaluate the feasibility of attacking such a system and create an attacker profile. For this purpose, we examine three available speech synthesis tools and discuss their usability. We use these tools and acquired knowledge to generate a dataset including deepfake speech and assess the resilience of voice biometrics systems against deepfakes. We prove that voice biometrics systems are indeed vulnerable to deepfake powered attacks. The most significant outcome is the proposal of text-dependent verification as a novel countermeasure for presented attacks. Text-dependent verification provides higher security than text-independent verification and can be used today as the simplest protection method against deepfakes. ER -
FIRC, Anton a Kamil MALINKA. The dawn of a text-dependent society: deepfakes as a threat to speech verification systems. Online. In \textit{SAC '22: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing}. Association for Computing Machinery, 2022, s.~1646--1655. Dostupné z: https://dx.doi.org/10.1145/3477314.3507013.
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