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

Suliman A Alsuhibany1

  • 1Department of Computer Science, College of Computer, Qassim University, Buridah 51452, Saudi Arabia.

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Summary
This summary is machine-generated.

This study introduces a secure Quick Response (QR) code system using digital watermarking and neural networks to prevent barcode fraud. The innovative method effectively identifies fraudulent QR codes, enhancing automated identification security.

Keywords:
barcode fraudinformation securityneural networkobject detectionsecure barcodewatermarking

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Area of Science:

  • Computer Science
  • Information Security
  • Data Integrity

Background:

  • Barcode technology, widely used for automated data capture, faces significant security vulnerabilities, particularly barcode substitution fraud.
  • Existing barcode systems lack robust mechanisms to prevent tampering and ensure data authenticity.
  • The increasing reliance on automated identification systems necessitates advanced security solutions.

Purpose of the Study:

  • To develop and evaluate an innovative system for secure Quick Response (QR) code generation and verification.
  • To enhance the integrity of QR codes against unauthorized modification and fraud.
  • To introduce a neural network-based authentication model for verifying QR code legitimacy.

Main Methods:

  • Implementation of a digital watermarking technique to embed tamper-resistant information within QR codes.
  • Development of a neural network-based authentication model for QR code verification.
  • Experimental evaluation using a dataset of 5000 QR code samples.

Main Results:

  • The proposed system demonstrated high accuracy in distinguishing between genuine and fraudulent QR codes.
  • Digital watermarking successfully enhanced QR code integrity, making them more resistant to tampering.
  • The neural network model proved effective in authenticating scanned QR codes.

Conclusions:

  • The developed system offers an effective solution for preventing QR code fraud in real-world applications.
  • Digital watermarking and neural network-based authentication significantly improve the security of automated identification systems.
  • This research contributes to enhancing the trustworthiness of barcode-based data capture processes.