SMNDNet for Multiple Types of Deepfake Image Detection

The majority of current deepfake detection methods are constrained to identifying one or two specific types of counterfeit images, which limits their ability to keep pace with the rapid advancements in deepfake technology. Th... | Find, read and cite all the research you need on Tech Science Press

Authors

Publication Details

Journal
Computers, materials & continua/Computers, materials & continua (Print)
Published
2025-01-01
DOI
https://doi.org/10.32604/cmc.2025.063141
Citations
1
Primary Topic
Digital Media Forensic Detection
Type
article
Field-Weighted Citation Impact
1.28

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

SMNDNet for Multiple Types of Deepfake Image Detection

Zinian Liu, Mingtao Guo, Jianghua Li, Ruidong Han et al.
1 citations
Computers, materials & continua/Computers, materials & continua (Print)
Digital Media Forensic Detection
1.28
article

SMNDNet for Multiple Types of Deepfake Image Detection

Zinian Liu, Mingtao Guo, Jianghua Li, Ruidong Han, Xiaofeng Wang, Qin Wang
article en
1 citations

Abstract

The majority of current deepfake detection methods are constrained to identifying one or two specific types of counterfeit images, which limits their ability to keep pace with the rapid advancements in deepfake technology. Th... | Find, read and cite all the research you need on Tech Science Press

Computers, materials & continua/Computers, materials & continua (Print)Vol. 83(3)
National Natural Science Foundation of China
Openalex Percentile: Top 22%
Digital Media Forensic Detection
1.28
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.