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

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.