WaveFake: 一组数据，以促进音频深做检测

深度生成建模有可能对社会造成重大危害。认识到这一威胁，对检测所谓的"深制"进行了大量研究。这项研究通常侧重于图像领域，而探索生成音频信号的研究迄今被忽视。在本文中，我们为缩小这一差距作出了三项关键贡献。首先，我们为研究人员介绍用于分析音频信号的常见信号处理技术。其次，我们提出了一套新颖的数据集，为此我们从五种不同的网络架构中收集了九套样本，跨越两种语言。最后，我们为从业者提供两个基线模型，从信号处理社区采用，以促进这方面的进一步研究。

原文题目：WaveFake: A Data Set to Facilitate Audio Deepfake Detection

原文：Deep generative modeling has the potential to cause significant harm to society. Recognizing this threat, a magnitude of research into detecting so-called "Deepfakes" has emerged. This research most often focuses on the image domain, while studies exploring generated audio signals have, so-far, been neglected. In this paper we make three key contributions to narrow this gap. First, we provide researchers with an introduction to common signal processing techniques used for analyzing audio signals. Second, we present a novel data set, for which we collected nine sample sets from five different network architectures, spanning two languages. Finally, we supply practitioners with two baseline models, adopted from the signal processing community, to facilitate further research in this area.