R. Reyes, Myriam J. Polinar, Richardson M. Dasalla, Godofredo S. Zapanta, Mark P. Melegrito, R. R. Maaliw
{"title":"基于计算机视觉的深度学习签名伪造检测系统:一种监督学习方法","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":"{\"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}","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}
Computer Vision-Based Signature Forgery Detection System Using Deep Learning: A Supervised Learning Approach
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.