版权说明 操作指南
首页 > 成果 > 详情

A data augmentation framework by mining structured features for fake face image detection

认领
导出
Link by DOI
反馈
分享
QQ微信 微博
成果类型:
期刊论文
作者:
Guo, Zhiqing;Yang, Gaobo;Wang, Dewang;Zhang, Dengyong
通讯作者:
Yang, Gaobo(yanggaobo@hnu.edu.cn)
作者机构:
[Wang, Dewang; Guo, Zhiqing; Yang, Gaobo] Hunan Univ, Sch Comp Sci & Elect Engn, Changsha 410082, Peoples R China.
[Zhang, Dengyong] Changsha Univ Sci & Technol, Sch Comp & Commun Engn, Changsha 410114, Peoples R China.
通讯机构:
[Gaobo Yang] H
Hunan University, School of Computer Science and Electronic Engineering, Changsha, 410082, China
语种:
英文
关键词:
Data augmentation;Deepfake detection;Face forgery detection;Structured forgery clues
期刊:
Computer Vision and Image Understanding
ISSN:
1077-3142
年:
2023
卷:
226
页码:
103587
基金类别:
CRediT authorship contribution statement Zhiqing Guo: Conceptualization, Methodology, Software, Validation, Writing – original draft. Gaobo Yang: Resources, Supervision, acquisition, Writing – review & editing. Dewang Wang: Data curation, Investigation. Dengyong Zhang: Writing – review & editing.
机构署名:
本校为其他机构
院系归属:
计算机与通信工程学院
摘要:
For fake face image detection, most existing detectors exploit local artifacts, ignoring the mining of structured forgery clues existed in global images, which greatly constrains detection performance. In this work, we verify the importance of structured forgery clues for fake face image detection, and present a new data augmentation framework called Mining Structured Features (MSF) to promote the convolutional neural network (CNN) based detector. Specifically, MSF generates a position-sensitive artifact map, which is exploited to divide a cand...

反馈

验证码:
看不清楚,换一个
确定
取消

成果认领

标题:
用户 作者 通讯作者
请选择
请选择
确定
取消

提示

该栏目需要登录且有访问权限才可以访问

如果您有访问权限,请直接 登录访问

如果您没有访问权限,请联系管理员申请开通

管理员联系邮箱:yun@hnwdkj.com