Detail publikace

Deepfake Speech Detection: A Spectrogram Analysis

FIRC, A. MALINKA, K. HANÁČEK, P.

Originální název

Deepfake Speech Detection: A Spectrogram Analysis

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

The current voice biometric systems have no natural mechanics to defend against deepfake spoofing attacks. Thus, supporting these systems with a deepfake detection solution is necessary. One of the latest approaches to deepfake speech detection is representing speech as a spectrogram and using it as an input for a deep neural network. This work thus analyzes the feasibility of different spectrograms for deepfake speech detection. We compare types of them regarding their performance, hardware requirements, and speed. We show the majority of the spectrograms are feasible for deepfake detection. However, there is no general, correct answer to selecting the best spectrogram. As we demonstrate, different spectrograms are suitable for different needs.

Klíčová slova

Deepfake, Speech, Image-based, Deepfake Detection, Spectrogram

Autoři

FIRC, A.; MALINKA, K.; HANÁČEK, P.

Vydáno

8. 4. 2024

Nakladatel

Association for Computing Machinery

Místo

Avila

ISBN

979-8-4007-0243-3

Kniha

Proceedings of the ACM Symposium on Applied Computing

Strany od

1312

Strany do

1320

Strany počet

9

URL

https://dl.acm.org/doi/10.1145/3605098.3635911

BibTex

@inproceedings{BUT188028,
  author="Anton {Firc} and Kamil {Malinka} and Petr {Hanáček}",
  title="Deepfake Speech Detection: A Spectrogram Analysis",
  booktitle="Proceedings of the ACM Symposium on Applied Computing",
  year="2024",
  pages="1312--1320",
  publisher="Association for Computing Machinery",
  address="Avila",
  doi="10.1145/3605098.3635911",
  isbn="979-8-4007-0243-3",
  url="https://dl.acm.org/doi/10.1145/3605098.3635911"
}