A hyperspectral unmixing approach for ink mismatch detection in unbalanced clusters

Faryal Aurooj Nasir , Salman Liaquat , Khurram Khurshid , Nor Muzlifah Mahyuddin
{"title":"A hyperspectral unmixing approach for ink mismatch detection in unbalanced clusters","authors":"Faryal Aurooj Nasir ,&nbsp;Salman Liaquat ,&nbsp;Khurram Khurshid ,&nbsp;Nor Muzlifah Mahyuddin","doi":"10.1016/j.jiixd.2024.01.004","DOIUrl":null,"url":null,"abstract":"<div><p>Detecting ink mismatch is a significant challenge in verifying the authenticity of documents, especially when dealing with uneven ink distribution. Conventional imaging methods frequently fail to distinguish visually similar inks. Our study presents a novel hyperspectral unmixing approach to detect ink mismatches in unbalanced clusters. The proposed method identifies unique spectral characteristics of different inks employing k-means clustering and Gaussian mixture models (GMMs) to perform color segmentation on different ink types and utilizes elbow estimation and silhouette coefficient to evaluate the number of inks estimation precisely. For a more accurate estimation of quantity, which is generally not an attribute of clustering methods, we employed entropy calculations in the red, green, and blue depth channels for precise abundance estimation of ink. This unique combination of basic techniques in conjunction exhibits better efficacy in performing ink unmixing and provides a real-world document forensic solution compared to current methods that rely on assumptions like prior knowledge of the inks used in a document and deep learning-based methods that rely heavily on abundant training datasets. We evaluate our approach on the iVision handwritten hyperspectral images dataset (iVision HHID), which is a comprehensive and rich dataset that surpasses the commonly-used UWA writing inks hyperspectral images (WIHSI) database in size and diversity. This study has accomplished the unmixing task with three main challenges: unmixing of diverse ink spectral signatures (149 spectral bands instead of 33 bands in the previous dataset), without using prior knowledge and assumptions about the number of inks used in the questioned document, and not requiring large training data for performing unmixing. Furthermore, the security of the proposed document authentication methodology to address the likelihood of forgeries or manipulations in questioned documents is enhanced as compared to previous works relying on known inks and known spectrum. Randomization techniques and anomaly detection mechanisms are used in our methodology which increases the difficulty for adversaries to predict and manipulate specific aspects of the input data in questioned documents, thereby enhancing the robustness of our method. The code for conducting this research can be accessed at <span>GitHub repository</span><svg><path></path></svg>.</p></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"2 2","pages":"Pages 177-190"},"PeriodicalIF":0.0000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949715924000040/pdfft?md5=3d98b093a0be134b496feff3d3fa509c&pid=1-s2.0-S2949715924000040-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Information and Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2949715924000040","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Detecting ink mismatch is a significant challenge in verifying the authenticity of documents, especially when dealing with uneven ink distribution. Conventional imaging methods frequently fail to distinguish visually similar inks. Our study presents a novel hyperspectral unmixing approach to detect ink mismatches in unbalanced clusters. The proposed method identifies unique spectral characteristics of different inks employing k-means clustering and Gaussian mixture models (GMMs) to perform color segmentation on different ink types and utilizes elbow estimation and silhouette coefficient to evaluate the number of inks estimation precisely. For a more accurate estimation of quantity, which is generally not an attribute of clustering methods, we employed entropy calculations in the red, green, and blue depth channels for precise abundance estimation of ink. This unique combination of basic techniques in conjunction exhibits better efficacy in performing ink unmixing and provides a real-world document forensic solution compared to current methods that rely on assumptions like prior knowledge of the inks used in a document and deep learning-based methods that rely heavily on abundant training datasets. We evaluate our approach on the iVision handwritten hyperspectral images dataset (iVision HHID), which is a comprehensive and rich dataset that surpasses the commonly-used UWA writing inks hyperspectral images (WIHSI) database in size and diversity. This study has accomplished the unmixing task with three main challenges: unmixing of diverse ink spectral signatures (149 spectral bands instead of 33 bands in the previous dataset), without using prior knowledge and assumptions about the number of inks used in the questioned document, and not requiring large training data for performing unmixing. Furthermore, the security of the proposed document authentication methodology to address the likelihood of forgeries or manipulations in questioned documents is enhanced as compared to previous works relying on known inks and known spectrum. Randomization techniques and anomaly detection mechanisms are used in our methodology which increases the difficulty for adversaries to predict and manipulate specific aspects of the input data in questioned documents, thereby enhancing the robustness of our method. The code for conducting this research can be accessed at GitHub repository.

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
用于非平衡集群中油墨错配检测的高光谱非混合方法
检测油墨不匹配是验证文件真伪的一大挑战,尤其是在油墨分布不均匀的情况下。传统的成像方法经常无法区分视觉上相似的油墨。我们的研究提出了一种新颖的高光谱非混合方法,用于检测不平衡集群中的油墨错配。所提出的方法利用 K 均值聚类和高斯混合模型(GMMs)来识别不同油墨的独特光谱特征,从而对不同类型的油墨进行颜色分割,并利用肘部估计和剪影系数来精确评估油墨估计数量。为了更精确地估算数量(这通常不是聚类方法的特性),我们在红色、绿色和蓝色深度通道中采用了熵计算,以精确估算墨水的丰度。与依赖文档中所用墨水的先验知识等假设的现有方法和严重依赖丰富训练数据集的基于深度学习的方法相比,这种将基本技术结合在一起的独特方法在进行墨水解混合时表现出更好的功效,并提供了一种真实世界的文档取证解决方案。我们在 iVision 手写高光谱图像数据集(iVision HHID)上评估了我们的方法,该数据集全面而丰富,在规模和多样性上超过了常用的 UWA 书写墨水高光谱图像(WIHSI)数据库。这项研究在完成非混合任务时面临三大挑战:非混合多种墨水光谱特征(149 个光谱带而不是之前数据集中的 33 个带),不使用关于问题文档中使用的墨水数量的先验知识和假设,以及执行非混合时不需要大量训练数据。此外,与之前依赖已知油墨和已知光谱的工作相比,所提出的文件认证方法的安全性得到了提高,可以解决受质疑文件中可能存在的伪造或篡改问题。我们的方法采用了随机化技术和异常检测机制,增加了对手预测和篡改问题文档中输入数据特定方面的难度,从而增强了我们方法的鲁棒性。本研究的代码可在 GitHub 存储库中获取。
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
Editorial Board Editorial Board Secure performance comparison for NOMA: Reconfigurable intelligent surface or amplify-and-forward relay? Editorial Board Structural knowledge-driven meta-learning for task offloading in vehicular networks with integrated communications, sensing and computing
×
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