A New ID Verification and Image Recognition Framework for Risk Management

Abstract

In the era where identity information is widely applicable in modern society, accurate identity authentication has become a focus of social attention, whether it is in the fields of finance, transportation, insurance, etc. In Australia, driver's licenses are widely used as a form of identification. Nevertheless, there is a growing trend of fraudsters creating counterfeit driver's licenses, deceiving both government entities and financial institutions, and causing substantial financial losses. This paper presents a new framework tailored for driver's license identity portrait verification. Utilizing a deep learning model and advanced preprocessing techniques, the framework is specifically designed to enhance risk management effectiveness. Experimental results indicate that the proposed Convolutional Neural Network modeling framework with Error Level Analysis (ELA) preprocessing exhibit notably higher accuracy compared to the traditional models.