R. Reyes, Myriam J. Polinar, Richardson M. Dasalla, Godofredo S. Zapanta, Mark P. Melegrito, R. R. Maaliw
{"title":"Computer Vision-Based Signature Forgery Detection System Using Deep Learning: A Supervised Learning Approach","authors":"R. Reyes, Myriam J. Polinar, Richardson M. Dasalla, Godofredo S. Zapanta, Mark P. Melegrito, R. R. Maaliw","doi":"10.1109/CONECCT55679.2022.9865776","DOIUrl":null,"url":null,"abstract":"Authentication is a crucial aspect of data security. It is one of the most important issues of our time. As technology advances, our interactions with machines are becoming increasingly automated. As a result, for a variety of security concerns, the demand for authentication is rapidly expanding. As a result, biometric-based authentication has become extremely popular. It has a significant edge over other approach. However, because different ways are utilized to verify people, this incidence is not a substitute for a problem. Signatures were one of the first commonly utilized biometric traits for identifying people. This paper describes a method for simplifying signature verification by preprocessing signatures. It also included a novel deep learning-based method for detecting faked signatures. With an accuracy of 85-95 %, the proposed method detects forgeries.","PeriodicalId":380005,"journal":{"name":"2022 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CONECCT55679.2022.9865776","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
Authentication is a crucial aspect of data security. It is one of the most important issues of our time. As technology advances, our interactions with machines are becoming increasingly automated. As a result, for a variety of security concerns, the demand for authentication is rapidly expanding. As a result, biometric-based authentication has become extremely popular. It has a significant edge over other approach. However, because different ways are utilized to verify people, this incidence is not a substitute for a problem. Signatures were one of the first commonly utilized biometric traits for identifying people. This paper describes a method for simplifying signature verification by preprocessing signatures. It also included a novel deep learning-based method for detecting faked signatures. With an accuracy of 85-95 %, the proposed method detects forgeries.