The DKU-DUKEECE System for the Manipulation Region Location Task of ADD 2023
This work addresses audio deepfake detection for security applications, but it is incremental as it builds on existing methods for a specific competition task.
The paper tackled the problem of locating manipulated regions in audio deepfakes for the ADD 2023 challenge, achieving first rank with an 82.23% sentence accuracy, an F1 score of 60.66%, and a final score of 0.6713.
This paper introduces our system designed for Track 2, which focuses on locating manipulated regions, in the second Audio Deepfake Detection Challenge (ADD 2023). Our approach involves the utilization of multiple detection systems to identify splicing regions and determine their authenticity. Specifically, we train and integrate two frame-level systems: one for boundary detection and the other for deepfake detection. Additionally, we employ a third VAE model trained exclusively on genuine data to determine the authenticity of a given audio clip. Through the fusion of these three systems, our top-performing solution for the ADD challenge achieves an impressive 82.23% sentence accuracy and an F1 score of 60.66%. This results in a final ADD score of 0.6713, securing the first rank in Track 2 of ADD 2023.