Audio deepfake detection is an emerging topic, which was included in the ASVspoof 2021. However, the recent shared tasks have not covered many real-life and challenging scenarios. The first Audio Deep synthesis Detection challenge (ADD) was motivated to fill in the gap. The ADD 2022 includes three tracks: low-quality fake audio detection (LF), partially fake audio detection (PF) and audio fake game (FG). The LF track focuses on dealing with bona fide and fully fake utterances with various real-world noises etc. The PF track aims to distinguish the partially fake audio from the real. The FG track is a rivalry game, which includes two tasks: an audio generation task and an audio fake detection task. In this paper, we describe the datasets, evaluation metrics, and protocols. We also report major findings that reflect the recent advances in audio deepfake detection tasks.

翻译：音频深度伪造检测是一个新兴课题，已被纳入ASVspoof 2021。然而，近期的共享任务尚未涵盖许多现实且具有挑战性的场景。首届音频深度合成检测挑战赛（ADD）旨在填补这一空白。ADD 2022包含三个赛道：低质量伪造音频检测（LF）、部分伪造音频检测（PF）和音频伪造博弈（FG）。LF赛道专注于处理包含各类现实世界噪声等的真实语音和完全伪造语音。PF赛道旨在区分部分伪造音频与真实音频。FG赛道是对抗性博弈，包含两项任务：音频生成任务和音频伪造检测任务。本文描述了相关数据集、评估指标和协议。我们还报告了反映音频深度伪造检测任务最新进展的主要发现。