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Forgery Detection in Aadhaar-based KYC using Deep Learning

Published: 07 June 2024 Publication History

Abstract

With rise of digitization, usage of digital documents and images have become indispensable for identity verification and address authentication. However, the prevalence of advanced image editing tools in today’s digital era has led to frequent doubts about the authenticity of digital images and documents. Forgery has become an inevitable concern in the realm of digital images, encompassing both the authenticity and integrity of the images. Image forgery presents a noteworthy threat in the Telecom industry, specifically when forged Aadhaar card images are utilized as proof of identity or address for sim activation. While e-KYC (Know Your Customer) remains the safest way to get the authentication done, document-based KYC (d-KYC) is still one of the approved methods for KYC as recommended by government regulatory body for Indian Telecom services, leading to threat of utilisation of forged documents for KYC.
This paper proposes a novel approach for detecting forgery in Aadhaar cards by leveraging Deep Convolutional Neural Network architectures and identifying VGG16 as the optimal model for the presented use-case. The proposed solution has been deployed across almost all states of India for real-time detection of forgery in Aadhaar cards examining approximately 100K Aadhaar cards daily. Through this solution, we aim to contribute to the enhancement of authentication mechanisms and address the growing concerns associated with image forgery in digital authentication.

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ICMLC '24: Proceedings of the 2024 16th International Conference on Machine Learning and Computing
February 2024
757 pages
ISBN:9798400709234
DOI:10.1145/3651671
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

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Published: 07 June 2024

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Author Tags

  1. Computer Vision
  2. Convolution Neural Network
  3. Deep Convolutional Neural Network
  4. Digital Authentication
  5. Digital Image Forgery
  6. Image Classification
  7. KYC
  8. Telecom
  9. Template Forgery Detection

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