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20212025
most citedContinual Learning for Fake Audio Detection

35 citations · 150 across the 23 of their papers we have counts for

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Showing 2022Show all

6 papers · 1 filter

cs.SD2022

SceneFake: An Initial Dataset and Benchmarks for Scene Fake Audio Detection

Jiangyan Yi, Chenglong Wang, Jianhua Tao +5

Many datasets have been designed to further the development of fake audio detection. However, fake utterances in previous datasets are mostly generated by altering timbre, prosody,…

cs.SD2022★ 4 cited

EmoFake: An Initial Dataset for Emotion Fake Audio Detection

Yan Zhao, Jiangyan Yi, Jianhua Tao +3

Many datasets have been designed to further the development of fake audio detection, such as datasets of the ASVspoof and ADD challenges. However, these datasets do not consider a…

cs.SD2022★ 28 cited

An Initial Investigation for Detecting Vocoder Fingerprints of Fake Audio

Xinrui Yan, Jiangyan Yi, Jianhua Tao +5

Many effective attempts have been made for fake audio detection. However, they can only provide detection results but no countermeasures to curb this harm. For many related practic…

cs.SD2022

Fully Automated End-to-End Fake Audio Detection

Chenglong Wang, Jiangyan Yi, Jianhua Tao +6

The existing fake audio detection systems often rely on expert experience to design the acoustic features or manually design the hyperparameters of the network structure. However,…

cs.SD2022★ 2 cited

CFAD: A Chinese Dataset for Fake Audio Detection

Haoxin Ma, Jiangyan Yi, Chenglong Wang +5

Fake audio detection is a growing concern and some relevant datasets have been designed for research. However, there is no standard public Chinese dataset under complex conditions.…

cs.SD2022★ 1 cited

ADD 2022: the First Audio Deep Synthesis Detection Challenge

Jiangyan Yi, Ruibo Fu, Jianhua Tao +17

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.…