Year: 2024
Type: conference-paper
Abstract: As speech synthesis systems continue to make remarkable advances in recent years, the importance of robust deepfake detection systems that perform well in unseen systems has grown.In this paper, we propose a novel adaptive centroid shift (ACS) method that updates the centroid representation by continually shifting as the weighted average of bonafide representations.Our approach uses only bonafide samples to define their centroid, which can yield a specialized centroid for one-class learning.Integrating our ACS with one-class learning gathers bonafide representations into a single cluster, forming wellseparated embeddings robust to unseen spoofing attacks.Our proposed method achieves an equal error rate (EER) of 2.19% on the ASVspoof 2021 deepfake dataset, outperforming all existing systems.Furthermore, the t-SNE visualization illustrates that our method effectively maps the bonafide embeddings into a single cluster and successfully disentangles the bonafide and spoof classes.
Source: Interspeech (USB)
Authors: Hyun Myung Kim, Kangwook Jang, Hoirin Kim
Institution: Korea Advanced Institute of Science and Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: G4700831490, G7318517199
OpenAlex ID: W4402112426