基于深度分类器和小波变换的假图像检测

Stanislaw Osowski, Maciej Golgowski
{"title":"基于深度分类器和小波变换的假图像检测","authors":"Stanislaw Osowski, Maciej Golgowski","doi":"10.26636/jtit.2023.4.1336","DOIUrl":null,"url":null,"abstract":"The paper presents the computer system for detecting deep fake images in video films. The system is based on applyingcontinuous wavelet transformation combined with the ensemble of classifiers composed of a few convolutional neural networks of diversified architecture. Three different forms of forged images taken from the Face-Forensics++ database are considered in numerical experiments. The results of experiments on the application of the proposed system have shown good performance in comparison to other actual approaches to this problem.","PeriodicalId":38425,"journal":{"name":"Journal of Telecommunications and Information Technology","volume":"72 ","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep Classifiers and Wavelet Transformation for Fake Image Detection\",\"authors\":\"Stanislaw Osowski, Maciej Golgowski\",\"doi\":\"10.26636/jtit.2023.4.1336\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper presents the computer system for detecting deep fake images in video films. The system is based on applyingcontinuous wavelet transformation combined with the ensemble of classifiers composed of a few convolutional neural networks of diversified architecture. Three different forms of forged images taken from the Face-Forensics++ database are considered in numerical experiments. The results of experiments on the application of the proposed system have shown good performance in comparison to other actual approaches to this problem.\",\"PeriodicalId\":38425,\"journal\":{\"name\":\"Journal of Telecommunications and Information Technology\",\"volume\":\"72 \",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-10-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Telecommunications and Information Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.26636/jtit.2023.4.1336\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Telecommunications and Information Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.26636/jtit.2023.4.1336","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0

摘要

本文介绍了一种用于视频中深度假图像检测的计算机系统。该系统基于连续小波变换,结合由多个不同结构的卷积神经网络组成的分类器集合。在数值实验中考虑了从Face-Forensics++数据库中提取的三种不同形式的伪造图像。应用实验结果表明,与其他实际方法相比,该系统具有良好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Deep Classifiers and Wavelet Transformation for Fake Image Detection
The paper presents the computer system for detecting deep fake images in video films. The system is based on applyingcontinuous wavelet transformation combined with the ensemble of classifiers composed of a few convolutional neural networks of diversified architecture. Three different forms of forged images taken from the Face-Forensics++ database are considered in numerical experiments. The results of experiments on the application of the proposed system have shown good performance in comparison to other actual approaches to this problem.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Telecommunications and Information Technology
Journal of Telecommunications and Information Technology Engineering-Electrical and Electronic Engineering
CiteScore
1.20
自引率
0.00%
发文量
34
期刊最新文献
High-isolation Quad-port MIMO Antenna for 5G Applications A Generalized Learning Approach to Deep Neural Networks Increasing Parallelism in Forward-backward Distributed Algorithm for Finding Strongly Connected Components of Directed Graphs Analyzing Performance of THz Band Graphene-Based MIMO Antenna for 6G Applications Multiprobe Planar Near-field Range Antenna Measurement System with Improved Performance
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1