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    • Negative instance generation for cross-domain facial forgery detection

    • In the field of deep forgery detection, experts have proposed the cross domain face forgery detection model NIG-FFD, which effectively improves the performance of cross domain and local domain detection.
    • Vol. 30, Issue 2, Pages: 421-434(2025)   

      Received:27 March 2024

      Revised:13 May 2024

      Published:16 February 2025

    • DOI: 10.11834/jig.240160     

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  • Zhang Jing, Xu Pan, Liu Wenjun, Guo Xiaoxuan, Sun Fang. 2025. Negative instance generation for cross-domain facial forgery detection. Journal of Image and Graphics, 30(02):0421-0434 DOI: 10.11834/jig.240160.
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相关作者

Wenqi Zhuo 中国科学院大学人工智能学院;中国科学院自动化研究所智能感知与计算研究中心
Dongze Li 中国科学院大学人工智能学院;中国科学院自动化研究所智能感知与计算研究中心
Wei Wang 中国科学院自动化研究所智能感知与计算研究中心
Jing Dong 中国科学院自动化研究所智能感知与计算研究中心
Ying Li 华南农业大学数学与信息学院;广州市智慧农业重点实验室
Shan Bian 华南农业大学数学与信息学院;广州市智慧农业重点实验室
Chuntao Wang 华南农业大学数学与信息学院;广州市智慧农业重点实验室
Wei Lu 中山大学计算机学院

相关机构

School of Artificial Intelligence, University of Chinese Academy of Sciences
Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences
College of Mathematics and Informatics, South China Agricultural University
Guangzhou Key Laboratory of Intelligent Agriculture
School of Computer Science and Engineering,Sun Yat-sen University
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