Publication Details
Deepfakes as a threat to a speaker and facial recognition: an overview of tools and attack vectors
Face deepfakes, Speech deepfakes, Biometrics systems, Facial recognition, Speaker
recognition, Deepfake detection, Cybersecurity
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.
@article{BUT185123,
author="Anton {Firc} and Kamil {Malinka} and Petr {Hanáček}",
title="Deepfakes as a threat to a speaker and facial recognition: an overview of tools and attack vectors",
journal="Heliyon",
year="2023",
volume="9",
number="4",
pages="1--33",
doi="10.1016/j.heliyon.2023.e15090",
issn="2405-8440",
url="https://www.sciencedirect.com/science/article/pii/S2405844023022971"
}