Publication Details
Comprehensive Multiparametric Analysis of Human Deepfake Speech Recognition
Firc Anton, Ing. (DITS)
Šalko Milan, Ing. (DITS)
Prudký Daniel, Bc.
Radačovská Karolína, Bc.
Hanáček Petr, doc. Dr. Ing. (DITS)
Deepfake, Synthetic speech, Deepfake detection, Human perception, Speech quality,
Cybersecurity
In this paper, we undertake a novel two-pronged investigation into the human
recognition of deepfake speech, addressing critical gaps in existing research.
First, we pioneer an evaluation of the impact of prior information on deepfake
recognition, setting our work apart by simulating real-world attack scenarios
where individuals are not informed in advance of deepfake exposure. This approach
simulates the unpredictability of real-world deepfake attacks, providing
unprecedented insights into human vulnerability under realistic conditions.
Second, we introduce a novel metric to evaluate the quality of deepfake audio.
This metric facilitates a deeper exploration into how the quality of deepfake
speech influences human detection accuracy. By examining both the effect of prior
knowledge about deepfakes and the role of deepfake speech quality, our research
reveals the importance of these factors, contributes to understanding human
vulnerability to deepfakes, and suggests measures to enhance human detection
skills.
@article{BUT189344,
author="Kamil {Malinka} and Anton {Firc} and Milan {Šalko} and Daniel {Prudký} and Karolína {Radačovská} and Petr {Hanáček}",
title="Comprehensive Multiparametric Analysis of Human Deepfake Speech Recognition",
journal="Eurasip Journal on Image and Video Processing",
year="2024",
volume="2024",
number="24",
pages="25",
doi="10.1186/s13640-024-00641-4",
issn="1687-5281",
url="https://jivp-eurasipjournals.springeropen.com/articles/10.1186/s13640-024-00641-4"
}