Authors:
Mouna Rabhi, Spiridon Bakiras, Roberto Di Pietro
Where published:
ELSEVIER (Expert Systems with Applications)
Dataset names (used for):
- H-VOICE: used to train a machine learning system to classify original and fake voice recordings obtained with the imitation and Deep Voice algorithms
Some description of the approach:
The study explores vulnerabilities of audio deepfake detection systems to adversarial attacks, demonstrating that current methods like Deep4SNet can be manipulated to nearly 0% detection accuracy using GAN-based attacks. A new, generalizable defense mechanism is proposed to enhance system resilience.
The study explores vulnerabilities of audio deepfake detection systems to adversarial attacks, demonstrating that current methods like Deep4SNet can be manipulated to nearly 0% detection accuracy using GAN-based attacks. A new, generalizable defense mechanism is proposed to enhance system resilience.
Some description of the data:
The study uses a dataset of 6,672 histograms from both original and fake audio samples for training their detection models.
The study uses a dataset of 6,672 histograms from both original and fake audio samples for training their detection models.
Keywords:
Authentication, Adversarial attacks, Audio deepfake, Fake voice detection, GAN, Biometrics, Security
Instance Represent:
Audio histograms
Dataset Characteristics:
6,672 histograms from both original and fake audio samples
Subject Area:
Security of audio authentication systems
Associated Tools:
Deepfake audio detection
Feature Type:
Histograms
License: This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Last Accessed: 6/15/2024