## 1 Introduction  
With the advent of the digital age, user authentication technologies have become indispensable for accessing online services [1]. While offline identity verification often relies on tangible credentials such as Social Security Numbers (SSN), online environments necessitate remote and contactless authentication methods due to the absence of direct verification mechanisms. Among these, password-based authentication remains the most widely employed technique due to its ease of implementation [2, 3]. In this framework, users create their passwords during registration, and authentication is performed by matching the entered password with the stored credentials. Typically, passwords are entered via keyboard input, rendering keyboard data a critical asset that must be securely protected [4]. Despite its prevalence, password-based authentication is vulnerable to keyboard data attacks aimed at intercepting credentials. Attackers have successfully intercepted keyboard data using C/D-bit weakness and the RESEND command, leveraging the fact that PS/2 and USB interface keyboards were designed without robust security considerations [5]. As a result, these devices remain susceptible to hardware-level attacks. Additionally, attackers deploy a range of strategies across application, operating system, and hardware layers, complicating comprehensive defensive measures. Consequently, password-based authentication exhibits fundamental limitations in maintaining security, prompting the exploration of alternative approaches such as image-based authentication [6; 7]. Image-based authentication technologies enable users to input passwords by interacting with graphical images displayed on the screen, circumventing the need for keyboard input and thus mitigating risks associated with keyboard data attacks. However, these systems are not immune to exploitation. Specifically, if screen and mouse data are inadequately protected, attackers can extract authentication-related information. A notable vulnerability lies in the operating system's mouse position management, where attackers can track cursor movements by repeatedly invoking the GetCursorPos() function [8]. This highlights a significant security gap in image-based authentication systems. To address this vulnerability, defensive techniques utilizing the SetCursorPos() function [9] have been developed to generate synthetic mouse data, thereby obfuscating real input and confusing attackers. However, recent advancements in machine learning-based attack methods have enabled attackers to distinguish between real and synthetic mouse data by identifying patterns within the generated inputs, achieving classification accuracies of up to 99% [10; 11]. This paper proposes a novel approach to enhance the protection of mouse data in image-based authentication systems by leveraging Generative Adversarial Networks (GANs) [12]. The proposed method generates synthetic mouse data that closely resemble authentic user input, thereby diminishing the effectiveness of machine learning-based attacks. Furthermore, the system dynamically generates fake data in real time by training GANs on historical user mouse movement data and blending the synthetic data with live inputs. Experimental results demonstrate that the proposed technique reduces the classification accuracy of adversarial attacks from 99% to approximately 62%, thereby validating its effectiveness in securing mouse data. The contributions of this study are as follows:  
* The proposed technique addresses the shortcomings of existing mouse data protection methods, thus improving the security of image-based authentication systems by significantly reducing the success rates of machine learning-based attacks. * A dataset of real mouse movement data was constructed to facilitate the generation of realistic synthetic data using GANs. This dataset can be continuously updated with additional user data, enabling the production of increasingly sophisticated synthetic inputs to enhance defensive performance.