A Robust Graphical Authentication Framework Designed to Mitigate Shoulder Surfing Risks

In this project, a robust graphical authentication framework was developed to enhance user authentication security and reduce the risk of shoulder surfing attacks. Traditional text-based password systems are widely used but they are vulnerable to various security threats such as password guessing, phishing, and observation attacks. Shoulder surfing is one of the most common security risks in public environments where attackers may observe the user while entering their password.   To address this problem, the proposed system introduces a graphical password mechanism where users select a sequence of images instead of entering a text-based password. The system also incorporates a randomization mechanism that changes the position of images during each login attempt. This dynamic arrangement prevents attackers from easily identifying the correct password sequence even if they observe the login process . The experimental results show that the graphical authentication framework improves both security and usability. Users can remember graphical passwords more easily than complex alphanumeric passwords, making the authentication process simple and convenient. At the same time, the random image placement and click-point verification techniques significantly reduce the chances of shoulder surfing attacks.   Overall, the proposed system provides a secure and user-friendly authentication method for modern digital applications. The framework can be effectively implemented in web systems, banking applications, and other secure platforms where protecting user credentials is essential. By combining graphical passwords with dynamic security mechanisms, the system enhances authentication reliability and helps safeguard sensitive user information.

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