The Vicomtech partial deepfake detection and location system for the 2023 ADD Challenge
Autores: Juan Manuel Martín Doñas
Fecha: 19.08.2023
Abstract
This paper describes our submitted system to the 2023 Audio Deepfake Detection Challenge Track 2. This track focuses on locating the manipulated regions in partially fake audio. Our approach integrates a pre-trained Wav2Vec2 based feature extractor and two different downstream models for deepfake detection and audio clustering. While the detection module is composed of a simple but efficient downstream neural classification model, the clustering-based neural network was trained to first segment the audio and then discriminate between the original regions and the manipulated segments. The final segmentation was obtained by combining the clustering process with the decision score through the application of some post-processing strategies. We evaluate our system on the test set of the challenge track, showing good performance for partially fake detection and location in challenging environments. Our novel, simple and efficient approach ranked fourth in the mentioned challenge among sixteen participants.
BIB_text
title = {The Vicomtech partial deepfake detection and location system for the 2023 ADD Challenge},
pages = {37-42},
keywds = {
anti-spoofing; audio tampering; deep clustering; Partial deepfake; wav2vec
}
abstract = {
This paper describes our submitted system to the 2023 Audio Deepfake Detection Challenge Track 2. This track focuses on locating the manipulated regions in partially fake audio. Our approach integrates a pre-trained Wav2Vec2 based feature extractor and two different downstream models for deepfake detection and audio clustering. While the detection module is composed of a simple but efficient downstream neural classification model, the clustering-based neural network was trained to first segment the audio and then discriminate between the original regions and the manipulated segments. The final segmentation was obtained by combining the clustering process with the decision score through the application of some post-processing strategies. We evaluate our system on the test set of the challenge track, showing good performance for partially fake detection and location in challenging environments. Our novel, simple and efficient approach ranked fourth in the mentioned challenge among sixteen participants.
}
date = {2023-08-19},
}
