Detection For Non-Genuine Identification Documents

Calvin Yap; Koksheik Wong; Ganesh Krishnasamy; Ian K. T. Tan

doi:10.1109/ispacs57703.2022.10082800

Detection For Non-Genuine Identification Documents

Calvin Yap, Koksheik Wong, Ganesh Krishnasamy, Ian K. T. Tan

School of Mathematical & Computer Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Due to the recent pandemic, electronic know-your-customer (e-KYC) technologies have enabled business continuity for various sectors. In the case of Malaysia, the Malaysia National Identity card (MyKad) is often used as the official document for identity-verification purposes. However, when presenting MyKad online, the uploaded MyKad could be an image of the actual MyKad taken a few years back, a printout of a legit MyKad, or a digitally/physically tampered MyKad. This research aims to detect non-genuine MyKad. Specifi-cally, we consider the unique colour count, the surface rough-ness score (i.e., complexity), and local binary patterns (LBP) of the received MyKad image to make inferences. The proposed method achieves an accuracy of 97.71% and an Fl-score of 0.9773.

Original language	English
Title of host publication	2022 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)
Publisher	IEEE
ISBN (Electronic)	9798350332421
DOIs	https://doi.org/10.1109/ispacs57703.2022.10082800
Publication status	Published - 31 Mar 2023

Keywords

Tampering detection
e-KYC
local binary patterns
surface roughness
unique colour count

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Computer Vision and Pattern Recognition
Signal Processing

Access to Document

10.1109/ispacs57703.2022.10082800

Cite this

@inproceedings{b56e5ec5f4384d3989e417c366bc3ce2,

title = "Detection For Non-Genuine Identification Documents",

abstract = "Due to the recent pandemic, electronic know-your-customer (e-KYC) technologies have enabled business continuity for various sectors. In the case of Malaysia, the Malaysia National Identity card (MyKad) is often used as the official document for identity-verification purposes. However, when presenting MyKad online, the uploaded MyKad could be an image of the actual MyKad taken a few years back, a printout of a legit MyKad, or a digitally/physically tampered MyKad. This research aims to detect non-genuine MyKad. Specifi-cally, we consider the unique colour count, the surface rough-ness score (i.e., complexity), and local binary patterns (LBP) of the received MyKad image to make inferences. The proposed method achieves an accuracy of 97.71% and an Fl-score of 0.9773.",

keywords = "Tampering detection, e-KYC, local binary patterns, surface roughness, unique colour count",

author = "Calvin Yap and Koksheik Wong and Ganesh Krishnasamy and Tan, {Ian K. T.}",

note = "Funding Information: This work was partly supported by MDEC MyIndustryAI 979-8-3503-3242-1/22/$31.00 {\textcopyright}2022 IEEE Publisher Copyright: {\textcopyright} 2022 IEEE.",

year = "2023",

month = mar,

day = "31",

doi = "10.1109/ispacs57703.2022.10082800",

language = "English",

booktitle = "2022 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)",

publisher = "IEEE",

address = "United States",

}

TY - GEN

T1 - Detection For Non-Genuine Identification Documents

AU - Yap, Calvin

AU - Wong, Koksheik

AU - Krishnasamy, Ganesh

AU - Tan, Ian K. T.

PY - 2023/3/31

Y1 - 2023/3/31

N2 - Due to the recent pandemic, electronic know-your-customer (e-KYC) technologies have enabled business continuity for various sectors. In the case of Malaysia, the Malaysia National Identity card (MyKad) is often used as the official document for identity-verification purposes. However, when presenting MyKad online, the uploaded MyKad could be an image of the actual MyKad taken a few years back, a printout of a legit MyKad, or a digitally/physically tampered MyKad. This research aims to detect non-genuine MyKad. Specifi-cally, we consider the unique colour count, the surface rough-ness score (i.e., complexity), and local binary patterns (LBP) of the received MyKad image to make inferences. The proposed method achieves an accuracy of 97.71% and an Fl-score of 0.9773.

AB - Due to the recent pandemic, electronic know-your-customer (e-KYC) technologies have enabled business continuity for various sectors. In the case of Malaysia, the Malaysia National Identity card (MyKad) is often used as the official document for identity-verification purposes. However, when presenting MyKad online, the uploaded MyKad could be an image of the actual MyKad taken a few years back, a printout of a legit MyKad, or a digitally/physically tampered MyKad. This research aims to detect non-genuine MyKad. Specifi-cally, we consider the unique colour count, the surface rough-ness score (i.e., complexity), and local binary patterns (LBP) of the received MyKad image to make inferences. The proposed method achieves an accuracy of 97.71% and an Fl-score of 0.9773.

KW - Tampering detection

KW - e-KYC

KW - local binary patterns

KW - surface roughness

KW - unique colour count

UR - http://www.scopus.com/inward/record.url?scp=85152277686&partnerID=8YFLogxK

U2 - 10.1109/ispacs57703.2022.10082800

DO - 10.1109/ispacs57703.2022.10082800

M3 - Conference contribution

BT - 2022 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)

PB - IEEE

ER -

Detection For Non-Genuine Identification Documents

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

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