Boosting Deepfake Feature Extractors Using Unsupervised Domain Adaptation-Reference-Cited by-同舟云学术

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Boosting Deepfake Feature Extractors Using Unsupervised Domain Adaptation

Published:2024 Issue: Volume:31 Page:2010-2014
ISSN:1070-9908
Container-title:IEEE Signal Processing Letters
language:
Short-container-title:IEEE Signal Process. Lett.

Author:

Li Jicheng¹^ORCID,Hu Yongjian¹^ORCID,Liu Beibei¹^ORCID,Gong Zhaolong¹,Kang Xiangui²^ORCID

Affiliation:

1. School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China

2. Guangdong Key Laboratory of Information Security, and the School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China

Funder

Science and Technology Foundation of Guangzhou Huangpu Development District

Scientific Research Capability Improvement Program for Key Discipline Construction of Guangdong Province

National Fund Cultivation Project from China People's Police University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/97/10380231/10609491.pdf?arnumber=10609491

Reference34 articles.

1. Xception: Deep Learning with Depthwise Separable Convolutions

2. Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain

3. Generalizing Face Forgery Detection with High-frequency Features

4. Transformer-Based Feature Compensation and Aggregation for DeepFake Detection

5. Forgery-Domain-Supervised Deepfake Detection With Non-Negative Constraint

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