Abstract
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.
Abstract (translated)
本论文介绍了我们为第二个音频深度伪造检测挑战(ADD 2023)设计的系统,该系统专注于确定剪辑区域,重点关注如何定位修改区域。我们的方法是利用多个检测系统来识别剪辑区域并确定其真实性。具体而言,我们训练并整合了两个帧级别的系统:一个用于边界检测,另一个用于深度伪造检测。此外,我们使用训练唯一地基于真实数据的第三个VAE模型来确定给定音频片段的真实性。通过将这些三个系统的融合,我们ADD挑战中表现最佳的解决方案取得了令人印象深刻的82.23%语句准确性和60.66%的F1得分。这导致最终ADD得分为0.6713,确保了ADD 2023 track 2的第一排名。
URL
https://arxiv.org/abs/2308.10281
