AVoiD-DF: Audio-Visual Joint Learning for Detecting Deepfake-Reference-Cited by-同舟云学术

AVoiD-DF: Audio-Visual Joint Learning for Detecting Deepfake

Author:

Yang Wenyuan¹^ORCID,Zhou Xiaoyu²,Chen Zhikai³,Guo Bofei⁴,Ba Zhongjie⁵^ORCID,Xia Zhihua⁶^ORCID,Cao Xiaochun¹^ORCID,Ren Kui⁵^ORCID

Affiliation:

1. School of Cyber Science and Technology, Sun Yat-sen University, Shenzhen Campus, Shenzhen, China

2. School of Software Engineering, University of Electronic Science and Technology of China, Chengdu, China

3. Zhuque Laboratory, Tencent Security, Shenzhen, China

4. School of Electronic and Computer Engineering, Peking University, Shenzhen, China

5. College of Computer Science and Technology, Zhejiang University, Hangzhou, China

6. College of Cyber Security, Jinan University, Guangzhou, China

Funder

National Key Research and Development Program of China

Shenzhen Science and Technology Program

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Safety, Risk, Reliability and Quality

Link

Reference67 articles.

1. Transfer learning from speaker verification to multispeaker text-to-speech synthesis;jia;Proc Adv Neural Inf Process Syst,2018

4. FakeAVCeleb: A novel audio-video multimodal deepfake dataset;khalid;Proc Conf Neural Inf Process Syst Datasets Benchmarks Track,2021

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