ADD 2023: the Second Audio Deepfake Detection Challenge

Jiangyan Yi*, Jianhua Tao*, Ruibo Fu, Xinrui Yan, Chenglong Wang, Tao Wang, Chu Yuan Zhang, Xiaohui Zhang, Yan Zhao, Yong Ren, Le Xu, Junzuo Zhou, Hao Gu, Zhengqi Wen, Shan Liang, Zheng Lian, Shuai Nie, Haizhou Li

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

8 Citations (Scopus)

Abstract

Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the world to build new innovative technologies that can further accelerate and foster research on detecting and analyzing deepfake speech utterances. Different from previous challenges (e.g. ADD 2022), ADD 2023 focuses on surpassing the constraints of binary real/fake classification, and actually localizing the manipulated intervals in a partially fake speech as well as pinpointing the source responsible for generating any fake audio. Furthermore, ADD 2023 includes more rounds of evaluation for the fake audio game sub-challenge. The ADD 2023 challenge includes three subchallenges: audio fake game (FG), manipulation region location (RL) and deepfake algorithm recognition (AR). This paper describes the datasets, evaluation metrics, and protocols. Some findings are also reported in audio deepfake detection tasks.

Original languageEnglish
Pages (from-to)125-130
Number of pages6
JournalCEUR Workshop Proceedings
Volume3597
Publication statusPublished - 2023
Externally publishedYes
Event2023 Workshop on Deepfake Audio Detection and Analysis, DADA 2023 - Macao, China
Duration: 19 Aug 2023 → …

Keywords

  • Audio deepfake
  • audio fake game
  • deepfake algorithm recognition
  • fake detection
  • manipulation region location

Fingerprint

Dive into the research topics of 'ADD 2023: the Second Audio Deepfake Detection Challenge'. Together they form a unique fingerprint.

Cite this