Securing QR Codes Infrastructure Using AI to Detect Malicious Activity

With the advent of QR Codes, mobile devices, and computers, users can exchange information quickly but not se-curely. QR Codes-based attacks are rising rapidly, threatening the trust of this extensively used technology. Whether you use it at a restaurant, conference, or payment station, QR Codes inherently rely on user trust to safeguard transactions. Phishing scams using fake codes have increased by over 50%, while using QR vector malware attacks have increased by over 2400% since 2020 [3]–[5]. These alarming results suggest that integrity protections must be reinstated to restore public trust. This research addresses these challenges by leveraging Artificial intelligence (AI) and third-party services to detect QR Codes-based threats. We propose a Secure QR Codes system employing anomaly detection, risk signaling, and four machine-learning models trained on standard and malicious QR Codes. Using encryption engines, AI-based predictions, Application Programming Interface (API), proxy services, and AI assessment modules, the integrated solution identified over 96% simulated real-world attacks like phishing, exfiltration, injection, and code execution. Our approach detects QR Codes with modified structures. It provides a practical, non-proprietary, web-deployable solution to predict and mitigate these threats, restoring trust in QR Codes systems across various applications.