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3 kwi 2023 · This article suggests an unsupervised forgery detection framework that utilizes the correlations among the spectrums of documents’ matters in generating a weighted network for the tested documents. The network, then, is clustered using several unsupervised clustering algorithms.
25 mar 2022 · The main difference between both terms is that forgery reflects the intention of producing documents to defraud another one, while counterfeit is the intention of producing unauthorized imitation on a document [7, 8]. Most of the works in the literature focus on how to detect forged or counterfeit documents using similar approaches and techniques.
1 sty 2022 · However, this work provides a comprehensive review on the three main aspects of digital forensics; namely, image-processing-based, video-processing-based, and spectroscopy-based detection...
22 maj 2020 · Skilled forgery — Produced by a perpetrator that has access to one or more samples of the authentic signature and can imitate it after much practice. Skilled forgery is the most difficult of all forgeries to authenticate.
The main difference between both terms is that forgery reflects the intention of producing documents to defraud another one, while counterfeit is the intention of producing unauthorized imitation on a document [7, 8]. Most of the works in the literature focus on how to detect forged or counterfeit documents using similar approaches and techniques.
26 lut 2023 · Document forgery is a gateway to other types of fraud, as it can produce tampered supporting documents (invoices, birth certificates, receipts, payslips, etc.) that can lead to identity or tax fraud.
Introduction. The aim of this project is to improve the detection of offline signature forgeries. In this project we analyzed and used machine learning concepts to classify, identify and also differentiate between the fake and the original signature.
