Two-Stream Xception Structure Based on Feature Fusion for DeepFake Detection
Abstract DeepFake may have a crucial impact on people’s lives and reduce the trust in digital media, so DeepFake detection methods have developed rapidly. Most existing detection methods rely on single-space features (mostly RGB features), and there is still relatively little research on multi-space...
Main Authors: | Bin Wang, Liqing Huang, Tianqiang Huang, Feng Ye |
---|---|
Format: | Article |
Language: | English |
Published: |
Springer
2023-08-01
|
Series: | International Journal of Computational Intelligence Systems |
Subjects: | |
Online Access: | https://doi.org/10.1007/s44196-023-00312-8 |
Similar Items
-
Improving Detection of DeepFakes through Facial Region Analysis in Images
by: Fatimah Alanazi, et al.
Published: (2023-12-01) -
Media Forensics Considerations on DeepFake Detection with Hand-Crafted Features
by: Dennis Siegel, et al.
Published: (2021-07-01) -
A Comprehensive Review of DeepFake Detection Using Advanced Machine Learning and Fusion Methods
by: Gourav Gupta, et al.
Published: (2023-12-01) -
Enhancing the Generalization for Text Classification through Fusion of Backward Features
by: Dewen Seng, et al.
Published: (2023-01-01) -
Two-Stream RGB-D Human Detection Algorithm Based on RFB Network
by: Wenli Zhang, et al.
Published: (2020-01-01)