Scientific Publications
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.
Full article at https://zenodo.org/records/10021600http://addchallenge.cn/files/2023/pdf/p37-Mart%C3%ADn-Do%C3%B1as.pdf