Network Science as a Forgery Detection Tool in Digital Forensics

Alaa Amjed, Basim Mahmood, Khalid A. K. AlMukhtar
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引用次数: 2

Abstract

Forgery detection of documents is considered a challenging task in the field of digital forensics. The detection process is usually complex and needs a lot of stages, which consumes time and effort. The limitation of the literature lies in providing methods that can be efficiently and easily adopted with minimum cost. This work proposes a novel approach for detecting counterfeit/ forged documents. The proposed approach is based on network science approaches for analyzing documents’ ink spectrums aiming to detect whether a document was counterfeited or forged. To this end, Laser-Induced Breakdown Spectroscopy (LIBS) is used to retrieve the spectrums of the original and questioned documents. The extracted spectrums are formalized to create a dataset, which contains nodes (spectrums) and edges (correlations among spectrums). Then, the dataset is used to generate a network of spectrums that represented both the original and questioned documents. After that, the generated network is visualized and clustered. The detection process is mainly based on the information provided by network clusters (e.g., number of clusters). The results showed that the proposed approach was efficient in distinguishing documents. Moreover, the proposed approach was able to distinguish a document itself whether it was counterfeited by extracting the clusters of the questioned document.
网络科学作为数字取证中的伪造检测工具
文件伪造检测被认为是数字取证领域的一项具有挑战性的任务。检测过程通常比较复杂,需要很多阶段,耗费时间和精力。文献的局限性在于提供了以最小的成本高效、简便的方法。这项工作提出了一种检测伪造文件的新方法。该方法基于网络科学方法分析文件的墨水光谱,旨在检测文件是否伪造或伪造。为此,使用激光诱导击穿光谱(LIBS)来检索原始和可疑文件的光谱。提取的光谱被形式化以创建一个数据集,该数据集包含节点(光谱)和边缘(光谱之间的相关性)。然后,使用该数据集生成一个表示原始文档和被质疑文档的频谱网络。之后,生成的网络被可视化和集群化。检测过程主要基于网络集群提供的信息(如集群数量)。实验结果表明,该方法具有较好的文档识别效果。此外,所建议的方法能够通过提取被质疑文件的聚类来区分文件本身是否伪造。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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