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ADD 2022: the First Audio Deep Synthesis Detection Challenge

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arxiv 2202.08433 v3 pith:GR7DJ6I6 submitted 2022-02-17 cs.SD cs.LGeess.AS

classification cs.SDcs.LGeess.AS
keywords audiodetectionfaketaskstrackchallengedeepdeepfake
verification ladder T0 review T1 audit T2 compute T3 formal

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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Hidden-Domain Routing for All-Type Audio Deepfake Detection

    cs.SD 2026-08 accept novelty 5.0 of 10

    A router-then-specialist audio deepfake detector, which classifies audio type first and then applies type-specific models and thresholds, achieved 96.10% Macro-F1 and first place on AT-ADD Track2.

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