Forensic Camera Identification in Social Networks via Camera Fingerprint

Tzu-Yun Lin, Yu-Ru Wang
{"title":"Forensic Camera Identification in Social Networks via Camera Fingerprint","authors":"Tzu-Yun Lin, Yu-Ru Wang","doi":"10.4018/978-1-7998-8386-9.ch008","DOIUrl":null,"url":null,"abstract":"Image-related crimes cause the urgent demand for tracing the origin of digital images. The breakthrough is a passive detection method via photo response non-uniformity (PRNU) analysis proposed by Lukáš et al. Recently, digital images are often shot with handheld devices (such as smartphones) and transmitted using social media (such as LINE). Most of the images are distorted (such as compressed and resized) during transmission. Previous studies are less focused on the impact of transmission compression through social networks. Thirty-one different Apple mobile phones were used to capture digital images in the experiment. Images were uploaded to the photo album via LINE software and then downloaded. The modified signed peak correlation energy (MSPCE) statistics is used to evaluate the correlation between the PRNU values of the disputed images and the pattern noise of the experimental devices. Experimental results show that the PRNU analysis method can effectively trace the source of the shot device using the distorted images which are compressed and resized during the transmission in LINE.","PeriodicalId":281747,"journal":{"name":"Technologies to Advance Automation in Forensic Science and Criminal Investigation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Technologies to Advance Automation in Forensic Science and Criminal Investigation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/978-1-7998-8386-9.ch008","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Image-related crimes cause the urgent demand for tracing the origin of digital images. The breakthrough is a passive detection method via photo response non-uniformity (PRNU) analysis proposed by Lukáš et al. Recently, digital images are often shot with handheld devices (such as smartphones) and transmitted using social media (such as LINE). Most of the images are distorted (such as compressed and resized) during transmission. Previous studies are less focused on the impact of transmission compression through social networks. Thirty-one different Apple mobile phones were used to capture digital images in the experiment. Images were uploaded to the photo album via LINE software and then downloaded. The modified signed peak correlation energy (MSPCE) statistics is used to evaluate the correlation between the PRNU values of the disputed images and the pattern noise of the experimental devices. Experimental results show that the PRNU analysis method can effectively trace the source of the shot device using the distorted images which are compressed and resized during the transmission in LINE.
基于摄像头指纹的社交网络法医摄像头识别
图像犯罪引发了对数字图像溯源的迫切需求。突破口是Lukáš等人提出的通过光响应非均匀性(PRNU)分析的被动检测方法。最近,数码图像通常是用手持设备(如智能手机)拍摄的,并通过社交媒体(如LINE)传播。在传输过程中,大多数图像都是扭曲的(如压缩和调整大小)。以往的研究较少关注通过社交网络传输压缩的影响。在实验中,31款不同的苹果手机被用来捕捉数字图像。照片通过LINE软件上传到相册,然后下载。利用改进的符号峰值相关能(MSPCE)统计量来评估争议图像的PRNU值与实验设备的模式噪声之间的相关性。实验结果表明,PRNU分析方法可以有效地利用在LINE传输过程中压缩和调整大小的畸变图像来跟踪射击装置的源。
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 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信