Comprehensive Multiparametric Analysis of Human Deepfake Speech Recognition

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F24%3APU151920" target="_blank" >RIV/00216305:26230/24:PU151920 - isvavai.cz</a>

Výsledek na webu

<a href="https://jivp-eurasipjournals.springeropen.com/articles/10.1186/s13640-024-00641-4" target="_blank" >https://jivp-eurasipjournals.springeropen.com/articles/10.1186/s13640-024-00641-4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s13640-024-00641-4" target="_blank" >10.1186/s13640-024-00641-4</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comprehensive Multiparametric Analysis of Human Deepfake Speech Recognition

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Comprehensive Multiparametric Analysis of Human Deepfake Speech Recognition

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Eurasip Journal on Image and Video Processing

ISSN

1687-5176

e-ISSN

1687-5281

Svazek periodika

2024

Číslo periodika v rámci svazku

24

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

25

Strana od-do

1-25

Kód UT WoS článku

001302501400001

EID výsledku v databázi Scopus

2-s2.0-85202737368