Modal-Guided Multi-Domain Inconsistency Learning for Face Forgery Detection
The remarkable development of deepfake models has facilitated the generation of fake content with various modalities, such as forged images, manipulated audio, and modified video with (or without) corresponding audio. However, many existing methods only analyze content with known and fixed modalitie...
Saved in:
Main Authors: | Zishuo Guo, Baopeng Zhang, Jack Fan, Zhu Teng, Jianping Fan |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2024-12-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/15/1/229 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Lip forgery detection via spatial-frequency domain combination
by: Jiaying LIN, et al.
Published: (2022-12-01) -
Forgery face detection method based on multi-domain temporal features mining
by: Chuntao ZHU, et al.
Published: (2023-06-01) -
Local Region Frequency Guided Dynamic Inconsistency Network for Deepfake Video Detection
by: Pengfei Yue, et al.
Published: (2024-09-01) -
Deepfake detection method based on patch-wise lighting inconsistency
by: Wenxuan WU, et al.
Published: (2023-02-01) -
Mf-net: multi-feature fusion network based on two-stream extraction and multi-scale enhancement for face forgery detection
by: Hanxian Duan, et al.
Published: (2024-11-01)