  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.
