Source Camera Identification Using WLBP Descriptor

Nasme Zandi, F. Razzazi
{"title":"Source Camera Identification Using WLBP Descriptor","authors":"Nasme Zandi, F. Razzazi","doi":"10.1109/MVIP49855.2020.9187484","DOIUrl":null,"url":null,"abstract":"In this paper we introduce a camera identification method using WLBP texture descriptor. This descriptor has previously been used for texture and face classifiers. In the proposed method, we proposed to use WLBP operator in camera classification application to identify the imaging camera. In our method, the two-dimensional histogram of Weber’s features and LBP for camera identification are investigated. For this purpose, experiments were conducted on Dresden database. The proposed method has reached the accuracy of 99.52% on nine digital cameras of different models. In compressed JPEG images with the compression quality factor of 70% the method reached the accuracy of 89.04%. The results indicate that the proposed method has a high degree of accuracy in comparison to other proposed method and exhibits relatively good robustness to compression.","PeriodicalId":255375,"journal":{"name":"2020 International Conference on Machine Vision and Image Processing (MVIP)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Conference on Machine Vision and Image Processing (MVIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MVIP49855.2020.9187484","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

In this paper we introduce a camera identification method using WLBP texture descriptor. This descriptor has previously been used for texture and face classifiers. In the proposed method, we proposed to use WLBP operator in camera classification application to identify the imaging camera. In our method, the two-dimensional histogram of Weber’s features and LBP for camera identification are investigated. For this purpose, experiments were conducted on Dresden database. The proposed method has reached the accuracy of 99.52% on nine digital cameras of different models. In compressed JPEG images with the compression quality factor of 70% the method reached the accuracy of 89.04%. The results indicate that the proposed method has a high degree of accuracy in comparison to other proposed method and exhibits relatively good robustness to compression.
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
使用WLBP描述符的源相机识别
本文介绍了一种基于WLBP纹理描述符的摄像机识别方法。这个描述符以前被用于纹理和人脸分类器。在该方法中,我们提出将WLBP算子应用于相机分类中,对成像相机进行识别。在我们的方法中,研究了韦伯特征的二维直方图和用于相机识别的LBP。为此,在德累斯顿数据库上进行了实验。该方法在9台不同型号的数码相机上,准确率达到99.52%。在压缩质量因子为70%的JPEG图像中,该方法的准确率达到89.04%。结果表明,与其他方法相比,该方法具有较高的精度,并且具有较好的压缩鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Offline Handwritten Signature Verification and Recognition Based on Deep Transfer Learning A High-Accuracy, Cost-Effective People Counting Solution Based on Visual Depth Data Source Camera Identification Using WLBP Descriptor Convolutional Neural Network for Building Extraction from High-Resolution Remote Sensing Images PCB Defect Detection Using Denoising Convolutional Autoencoders
×
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