Environment Sonification and Adaptive GUI for Secure Authentication
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Solution Overview
Problem
In the context of remote user interactions, there is a need for improved data security to prevent eavesdropping and data misappropriation by generating surrounding image sonification and dynamically adjusting graphical user interfaces based on the trustworthiness of the environment.
Innovation Solution
A system that uses a user's device to capture real-time images, analyze them using machine learning and computer vision, and sonify the environment, adjusting the graphical user interface accordingly to enhance security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time image analysis and sonification are implemented to verify environment safety, then data security is improved, but device complexity and computing resource usage increase
Solution Approach 1:
The system segments the environment verification process into distinct modules: image capture by camera, machine learning-based object recognition, computer vision analysis, and audio sonification. Each module handles a specific aspect of environment assessment, distributing computational complexity across specialized components rather than requiring one monolithic complex system.
Solution Approach 2:
The patent introduces an intermediary authentication system that mediates between the user and the data transmission process. The system captures environment images, analyzes them through ML and computer vision, generates sonification representations, and presents authentication challenges to the user. This intermediary layer verifies environment safety without requiring the user to directly manage the complex analysis processes.
2Reliability
If multiple authentication methods (voice, facial, physical characteristics) are used, then authentication reliability is improved, but authentication time and user interaction complexity increase
Solution Approach 1:
The authentication system dynamically adjusts which verification methods are applied based on the assessed environment risk level. In low-risk environments, the system may use simplified authentication with fewer checks. In high-risk environments, it escalates to multiple authentication factors including voice, facial recognition, and physical characteristic verification. This dynamic adaptation reduces unnecessary authentication time in safe environments while maintaining security in risky ones.
Solution Approach 2:
The system changes authentication parameters based on environment analysis results. When the ML model and computer vision analysis indicate a safe environment, the system lowers authentication stringency. When objects or conditions are detected that suggest potential eavesdropping or security threats, the system increases authentication parameters to require additional verification steps, thus adapting the authentication process to actual risk levels.
3Reliability
If the graphical user interface is dynamically adjusted based on environment trustworthiness, then data security is improved, but processing complexity increases
Solution Approach 1:
The system applies local quality by adjusting specific UI elements based on environment risk rather than changing the entire interface uniformly. When security risks are detected, the system locally modifies specific components such as adding security warnings near data entry fields, enabling recording indicators for microphone activity, or adjusting sensitivity of authentication prompts in specific interface regions. This targeted approach improves security without requiring complete interface redesign.
Solution Approach 2:
The system performs preliminary environment analysis and UI preparation before the actual data transmission or authentication process. The ML model pre-analyzes captured images to identify potential security threats, and the system pre-adjusts the GUI configuration based on this analysis. By the time the user interacts with the interface, the appropriate security measures and interface configurations are already in place, reducing real-time processing complexity during critical operations.
Data Source
AI summary
Systems, computer program products, and methods are described herein for improving data security by generating surrounding image sonification and dynamically adjusting graphical user interfaces. The present disclosure is configured to receive a data transmission request and at an authentication credential; identify a voice input, facial input data, and a physical characteristic input data; compare the voice input with a voice authentication, the facial input data with a facial authentication data, and the physical characteristic input data with a physical characteristic authentication; authenticate a user based on the comparison; receive an expected environment user input; receive at least one real-time image of the real-time geographic environment; analyze the at least one real-time image; generate a real-time geographic environment indication; transmit the real-time geographic environment indication; receive a real-time environment authentication; and authenticate the data transmission request based on the authentication of the user and the real-time environment authentication.