A Comparative Review of QR Code Scanner According to a Malicious URL Detection Framework

The rapid use of Quick Response (QR) codes in various sectors, including marketing, banking, and object identifications, underscores the pressing need for some techniques required for the to detect the external malicious attacks on QR code. This paper focus on the examine the different attack methods on QR code which affects the integrity and security of QR code. It reviews and compares existing detection techniques, such as QR shield, QsecR, BarAI and BarSec, among others, to safeguard against these vulnerabilities for different use cases. In this comprehensive analysis, the paper categorizes detection methodologies based on their operational paradigms, including cryptographic solutions, anomaly detection-based approaches. The comparative study is anchored on several evaluation metrics such as detection accuracy, computational efficiency, and ease of implementation, aiming to delineate the optimal methods suitable for varying scenarios and threat models. The review process adopts a systematic approach, critically analyzing each method's effectiveness against common QR code attacks like overlay, phishing, and malware injections. The paper also explores the integration of artificial intelligence and machine learning techniques to enhance detection capabilities and adaptability against evolving threats. The impacts of this work are multifaceted, offering significant contributions to both academic research and practical applications. By providing a detailed comparison of attack detection techniques, the paper aids in identifying gaps in current security measures and proposes directions for future research. Furthermore, the findings serve as a valuable resource for developers and security analysts in implementing more robust and efficient QR code security solutions, ultimately fostering a safer digital environment for users.