Computer Vision-Based Signature Forgery Detection System Using Deep Learning: A Supervised Learning Approach

R. Reyes, Myriam J. Polinar, Richardson M. Dasalla, Godofredo S. Zapanta, Mark P. Melegrito, R. R. Maaliw
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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.
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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
身份验证是数据安全的一个关键方面。这是我们这个时代最重要的问题之一。随着科技的进步,我们与机器的互动变得越来越自动化。因此,出于各种安全考虑,对身份验证的需求正在迅速扩大。因此,基于生物特征的身份验证变得非常流行。它比其他方法有明显的优势。然而,由于使用了不同的方法来验证人,因此这种发生率不能代替问题。签名是最早用于识别人的生物特征之一。本文描述了一种通过对签名进行预处理来简化签名验证的方法。它还包括一种新的基于深度学习的检测伪造签名的方法。该方法检测伪造品的准确率为85- 95%。
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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