Podrobný výpis o publikaci

FIRC, Anton a Kamil MALINKA. The dawn of a text-dependent society: deepfakes as a threat to speech verification systems. Online. In 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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Základní údaje
Originální název	The dawn of a text-dependent society: deepfakes as a threat to speech verification systems
Autoři	FIRC, Anton a Kamil MALINKA.
Vydání	SAC '22: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing, od s. 1646--1655, 2022.
Nakladatel	Association for Computing Machinery

Další údaje
Typ výsledku	Stať ve sborníku
Utajení	není předmětem státního či obchodního tajemství
Forma vydání	elektronická verze "online"
Doi	http://dx.doi.org/10.1145/3477314.3507013
Klíčová slova anglicky	deepfakes, speech verification, voice biometrics, machine learning, cybersecurity
Příznaky	Mezinárodní význam, Recenzováno
Změnil	Změnil: Mgr. Kamil Malinka, Ph.D., učo 49967. Změněno: 26. 8. 2022 12:34.

Anotace
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.

Anotace

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.