Innovative QR Code System for Tamper-Proof Generation and Fraud-Resistant Verification

Barcode technology is widely used as an automated identification system that enables rapid and efficient data capture, particularly in retail environments. Despite its practicality, barcode-based systems are increasingly vulnerable to security threats—most notably, barcode substitution fraud. To add...

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Main Author: Suliman A. Alsuhibany
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/25/13/3855
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author Suliman A. Alsuhibany
author_facet Suliman A. Alsuhibany
author_sort Suliman A. Alsuhibany
collection DOAJ
description Barcode technology is widely used as an automated identification system that enables rapid and efficient data capture, particularly in retail environments. Despite its practicality, barcode-based systems are increasingly vulnerable to security threats—most notably, barcode substitution fraud. To address these challenges, this paper presents an innovative system for the secure generation and verification of Quick Response (QR) codes using a digital watermarking technique. The proposed method embeds tamper-resistant information within QR codes, enhancing their integrity and making unauthorized modification more difficult. Additionally, a neural network-based authentication model was developed to verify the legitimacy of scanned QR codes. The system was evaluated through experimental testing on a dataset of 5000 QR samples. The results demonstrated high accuracy in distinguishing between genuine and fraudulent QR codes, confirming the system’s effectiveness in supporting fraud prevention in real-world applications.
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spelling doaj-art-cd9790623c04477d9f9fb868f1b574ca2025-08-20T03:16:43ZengMDPI AGSensors1424-82202025-06-012513385510.3390/s25133855Innovative QR Code System for Tamper-Proof Generation and Fraud-Resistant VerificationSuliman A. Alsuhibany0Department of Computer Science, College of Computer, Qassim University, Buridah 51452, Saudi ArabiaBarcode technology is widely used as an automated identification system that enables rapid and efficient data capture, particularly in retail environments. Despite its practicality, barcode-based systems are increasingly vulnerable to security threats—most notably, barcode substitution fraud. To address these challenges, this paper presents an innovative system for the secure generation and verification of Quick Response (QR) codes using a digital watermarking technique. The proposed method embeds tamper-resistant information within QR codes, enhancing their integrity and making unauthorized modification more difficult. Additionally, a neural network-based authentication model was developed to verify the legitimacy of scanned QR codes. The system was evaluated through experimental testing on a dataset of 5000 QR samples. The results demonstrated high accuracy in distinguishing between genuine and fraudulent QR codes, confirming the system’s effectiveness in supporting fraud prevention in real-world applications.https://www.mdpi.com/1424-8220/25/13/3855barcode fraudsecure barcodewatermarkingobject detectioninformation securityneural network
spellingShingle Suliman A. Alsuhibany
Innovative QR Code System for Tamper-Proof Generation and Fraud-Resistant Verification
Sensors
barcode fraud
secure barcode
watermarking
object detection
information security
neural network
title Innovative QR Code System for Tamper-Proof Generation and Fraud-Resistant Verification
title_full Innovative QR Code System for Tamper-Proof Generation and Fraud-Resistant Verification
title_fullStr Innovative QR Code System for Tamper-Proof Generation and Fraud-Resistant Verification
title_full_unstemmed Innovative QR Code System for Tamper-Proof Generation and Fraud-Resistant Verification
title_short Innovative QR Code System for Tamper-Proof Generation and Fraud-Resistant Verification
title_sort innovative qr code system for tamper proof generation and fraud resistant verification
topic barcode fraud
secure barcode
watermarking
object detection
information security
neural network
url https://www.mdpi.com/1424-8220/25/13/3855
work_keys_str_mv AT sulimanaalsuhibany innovativeqrcodesystemfortamperproofgenerationandfraudresistantverification