Deepfakes as a threat to a speaker and facial recognition: an overview of tools and attack vectors

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F23%3APU149350" target="_blank" >RIV/00216305:26230/23:PU149350 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S2405844023022971" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2405844023022971</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.heliyon.2023.e15090" target="_blank" >10.1016/j.heliyon.2023.e15090</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Deepfakes as a threat to a speaker and facial recognition: an overview of tools and attack vectors

Popis výsledku v původním jazyce

Deepfakes present an emerging threat in cyberspace. Recent developments in machine learning make deepfakes highly believable, and very difficult to differentiate between what is real and what is fake. Not only humans but also machines struggle to identify deepfakes. Current speaker and facial recognition systems might be easily fooled by carefully prepared synthetic media - deepfakes. We provide a detailed overview of the state-of-the-art deepfake creation and detection methods for selected visual and audio domains. In contrast to other deepfake surveys, we focus on the threats that deepfakes represent to biometrics systems (e.g., spoofing). We discuss both facial and speechdeepfakes, and for each domain, we define deepfake categories and their differences. For each deepfake category, we provide an overview of available tools for creation, datasets, and detection methods. Our main contribution is a definition of attack vectors concerning the differences between categories and reported real-world attacks to evaluate each category's threats to selected categories of biometrics systems.

Název v anglickém jazyce

Deepfakes as a threat to a speaker and facial recognition: an overview of tools and attack vectors

Popis výsledku anglicky

Deepfakes present an emerging threat in cyberspace. Recent developments in machine learning make deepfakes highly believable, and very difficult to differentiate between what is real and what is fake. Not only humans but also machines struggle to identify deepfakes. Current speaker and facial recognition systems might be easily fooled by carefully prepared synthetic media - deepfakes. We provide a detailed overview of the state-of-the-art deepfake creation and detection methods for selected visual and audio domains. In contrast to other deepfake surveys, we focus on the threats that deepfakes represent to biometrics systems (e.g., spoofing). We discuss both facial and speechdeepfakes, and for each domain, we define deepfake categories and their differences. For each deepfake category, we provide an overview of available tools for creation, datasets, and detection methods. Our main contribution is a definition of attack vectors concerning the differences between categories and reported real-world attacks to evaluate each category's threats to selected categories of biometrics systems.

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

2023

Kód důvěrnosti údajů

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

Název periodika

Heliyon

ISSN

2405-8440

e-ISSN

—

Svazek periodika

9

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

33

Strana od-do

1-33

Kód UT WoS článku

000998660600001

EID výsledku v databázi Scopus

2-s2.0-85151552922