short-paper

KID34K: A Dataset for Online Identity Card Fraud Detection

Authors:

Seung-Yeon Back,

Simon S. WooAuthors Info & Claims

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

Pages 5381 - 5385

https://doi.org/10.1145/3583780.3615122

Published: 21 October 2023 Publication History

Abstract

Though digital financial systems have provided users with convenient and accessible services, such as supporting banking or payment services anywhere, it is necessary to have robust security to protect against identity misuse. Thus, online digital identity (ID) verification plays a crucial role in securing financial services on mobile platforms. One of the most widely employed techniques for digital ID verification is that mobile applications request users to take and upload a picture of their own ID cards. However, this approach has vulnerabilities where someone takes pictures of the ID cards belonging to another person displayed on a screen, or printed on paper to be verified as the ID card owner. To mitigate the risks associated with fraudulent ID card verification, we present a novel dataset for classifying cases where the ID card images that users upload to the verification system are genuine or digitally represented. Our dataset is replicas designed to resemble real ID cards, making it available while avoiding privacy issues. Through extensive experiments, we demonstrate that our dataset is effective for detecting digitally represented ID card images, not only in our replica dataset but also in the dataset consisting of real ID cards. Our dataset is available at https://github.com/DASH-Lab/idcard_fraud_detection.

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Cited By

Tapia JDamer NBusch CEspin JBarrachina JRocamora AOcvirk KAlessio LBatagelj BPatwardhan SRamachandra RMudgalgundurao RRaja KSchulz DAravena C(2024)First Competition on Presentation Attack Detection on ID Card2024 IEEE International Joint Conference on Biometrics (IJCB)10.1109/IJCB62174.2024.10744475(1-10)Online publication date: 15-Sep-2024
https://doi.org/10.1109/IJCB62174.2024.10744475
Xie LWang YGuan HNag SGoel RSwamy NYang YXiao CPrisby JMaciejewski RZou J(2024)IDNet: A Novel Identity Document Dataset via Few-Shot and Quality-Driven Synthetic Data Generation2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825017(2244-2253)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825017

Index Terms

KID34K: A Dataset for Online Identity Card Fraud Detection
1. Computing methodologies
  1. Machine learning
2. Security and privacy
  1. Software and application security

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cover image ACM Conferences

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

October 2023

5508 pages

ISBN:9798400701245

DOI:10.1145/3583780

General Chairs:
Ingo Frommholz
University of Wolverhampton, UK
,
Frank Hopfgartner
University of Koblenz, Germany
,
Mark Lee
University of Birmingham, UK
,
Michael Oakes
University of Birmingham, UK
,
Program Chairs:
Mounia Lalmas
Spotify, UK
,
Min Zhang
Tsinghua University, China
,
Rodrygo Santos
Federal University of Minas Gerais, Brazil

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Publication History

Published: 21 October 2023

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CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management

October 21 - 25, 2023

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Cited By

Tapia JDamer NBusch CEspin JBarrachina JRocamora AOcvirk KAlessio LBatagelj BPatwardhan SRamachandra RMudgalgundurao RRaja KSchulz DAravena C(2024)First Competition on Presentation Attack Detection on ID Card2024 IEEE International Joint Conference on Biometrics (IJCB)10.1109/IJCB62174.2024.10744475(1-10)Online publication date: 15-Sep-2024
https://doi.org/10.1109/IJCB62174.2024.10744475
Xie LWang YGuan HNag SGoel RSwamy NYang YXiao CPrisby JMaciejewski RZou J(2024)IDNet: A Novel Identity Document Dataset via Few-Shot and Quality-Driven Synthetic Data Generation2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825017(2244-2253)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825017

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