Face Forgery Detection by 3D Decomposition and Composition Search

Author:

Zhu Xiangyu1ORCID,Fei Hongyan1,Zhang Bin2,Zhang Tianshuo1,Zhang Xiaoyu2,Li Stan Z.3ORCID,Lei Zhen1ORCID

Affiliation:

1. Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

2. Institute of Information Engineering, Chinese Academy of Science, Beijing, China

3. School of Engineering, Westlake University, Hangzhou, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Test-time Forgery Detection with Spatial-Frequency Prompt Learning;International Journal of Computer Vision;2024-08-13

2. A dual descriptor combined with frequency domain reconstruction learning for face forgery detection in deepfake videos;Forensic Science International: Digital Investigation;2024-06

3. Large-margin multiple kernel p-SVDD using Frank–Wolfe algorithm for novelty detection;Pattern Recognition;2024-04

4. Fingerprint Presentation Attack Detection by Region Decomposition;IEEE Transactions on Information Forensics and Security;2024

5. Advancements in Image Forgery Detection: A Comprehensive Survey;2023 Seventh International Conference on Image Information Processing (ICIIP);2023-11-22

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