Publication detail

The dawn of a text-dependent society: deepfakes as a threat to speech verification systems

FIRC, A. MALINKA, K.

Original Title

The dawn of a text-dependent society: deepfakes as a threat to speech verification systems

Type

conference paper

Language

English

Original Abstract

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.

Keywords

deepfakes, speech verification, voice biometrics, machine learning, cybersecurity

Authors

FIRC, A.; MALINKA, K.

Released

25. 4. 2022

Publisher

Association for Computing Machinery

Location

New York, NY

ISBN

978-1-4503-8713-2

Book

SAC '22: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing

Pages from

1646

Pages to

1655

Pages count

10

URL

https://dl.acm.org/doi/10.1145/3477314.3507013

BibTex

@inproceedings{BUT175833,
  author="Anton {Firc} and Kamil {Malinka}",
  title="The dawn of a text-dependent society: deepfakes as a threat to speech verification systems",
  booktitle="SAC '22: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing",
  year="2022",
  pages="1646--1655",
  publisher="Association for Computing Machinery",
  address="New York, NY",
  doi="10.1145/3477314.3507013",
  isbn="978-1-4503-8713-2",
  url="https://dl.acm.org/doi/10.1145/3477314.3507013"
}