Comprehensive Multiparametric Analysis of Human Deepfake Speech Recognition

Comprehensive Multiparametric Analysis of Human Deepfake Speech Recognition

WoS Article

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.

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.

Deepfake, Synthetic speech, Deepfake detection, Human perception, Speech quality, Cybersecurity

Deepfake, Synthetic speech, Deepfake detection, Human perception, Speech quality, Cybersecurity

MALINKA, K.; FIRC, A.; ŠALKO, M.; PRUDKÝ, D.; RADAČOVSKÁ, K.; HANÁČEK, P.

2025

30.08.2024

Springer Nature

1687-5281

EURASIP Journal on Image and Video Processing

2024

24

Swiss Confederation

1

25

25

https://jivp-eurasipjournals.springeropen.com/articles/10.1186/s13640-024-00641-4

http://hdl.handle.net/11012/252348

s13640-024-00641-4