标题： LENS-DF：长时噪声语音的深度伪造检测与时间定位 显示英文标题

标题： LENS-DF: Deepfake Detection and Temporal Localization for Long-Form Noisy Speech

摘要： 本研究介绍了LENS-DF，这是一种新颖且全面的训练和评估音频深度伪造检测和时间定位的方案，在复杂和现实的音频条件下进行。 该方案的生成部分以可控的方式从输入数据集中输出具有多个关键特征的音频，例如更长的持续时间、噪声条件以及包含多个说话人。 相应的检测和定位协议使用模型。 我们基于自监督学习前端和简单的后端进行实验。 结果表明，使用LENS-DF生成的数据训练的模型始终优于通过传统方案训练的模型，证明了LENS-DF在鲁棒音频深度伪造检测和定位中的有效性和实用性。 我们还对引入的变化进行了消融研究，探讨它们对领域内现实挑战的影响和相关性。 显示英文摘要

摘要： This study introduces LENS-DF, a novel and comprehensive recipe for training and evaluating audio deepfake detection and temporal localization under complicated and realistic audio conditions. The generation part of the recipe outputs audios from the input dataset with several critical characteristics, such as longer duration, noisy conditions, and containing multiple speakers, in a controllable fashion. The corresponding detection and localization protocol uses models. We conduct experiments based on self-supervised learning front-end and simple back-end. The results indicate that models trained using data generated with LENS-DF consistently outperform those trained via conventional recipes, demonstrating the effectiveness and usefulness of LENS-DF for robust audio deepfake detection and localization. We also conduct ablation studies on the variations introduced, investigating their impact on and relevance to realistic challenges in the field.