OCA:一种智能模型,用于改进基于生物特征的身份验证系统的安全漏洞

Sandeep Kumar Sharma, Anil Kumar, Rashmi Ashtagi, Rekha Jain
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引用次数: 0

摘要

生物识别技术是近年来应用最为广泛的身份认证系统之一。一般来说,生物识别系统是基于扫描图像的成像技术。该系统对图像进行处理,并与数据库进行比对,判断用户的真实性。身份验证和更快的响应是所有生物识别系统的关键目标,但它们存在一个严重的问题,即安全漏洞。本文介绍了一种基于元胞自动机的方法,该方法处理图像并识别可能的安全漏洞。该方法对图像的所有像素进行分析并映射一个规则,该规则通过定义图像对象的所有可能边缘对图像进行处理并生成灰度图像。灰度图像只包含二值,这样可以减少灰度图像与数据库的对比时间。本文在开源数据集上对该方法进行了实验,并使用认证参数对实验结果进行了验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
OCA: An intelligent model for improving security breach of biometric based authentication systems
In the recent era, biometrics is one of the most used authentication systems. Generally, biometric systems are based on imaging technology, which works on scanned images. This system processes the image and compares it with the database and decides authenticity of user. Authentication and faster response are the key objectives of all biometric systems, but they suffered from a serious issue i.e., security breach. In this article, a Cellular Automata based method is introduced, which processes the image and identify possible security breaches. The proposed method analysis all the pixels of the image and maps a rule, which processes the image and generates a gray image by defining all the possible edges of the image objects. The gray image contains only binary values, which may reduce the chance of breach and decrease the comparison time of gray image with database. In this paper, the proposed method is experimented on the open-source dataset and the results are validated using authentication parameters.
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