Environment-Based Security Proof Generation for Dynamic Authentication
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Solution Overview
Problem
Conventional graphical authentication systems are cumbersome, require users to remember selected points in a picture, and fail to recognize real-time environmental objects or relate them accurately, leading to reduced system accuracy and erroneous results.
Innovation Solution
A system and method that uses image acquisition and analysis to generate a unique security proof based on a user's environment, featuring a pair of keys generated from environmental images, which are stored and compared in real-time to verify secure access, incorporating computer vision techniques and artificial intelligence for feature extraction and comparison.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional graphical authentication systems require users to select and remember specific points in a picture, then security verification is achieved, but user convenience deteriorates and operation complexity increases
Solution Approach 1:
The system automatically captures images of the user's environment and extracts features without requiring user selection or input. The environment itself serves as the authentication credential, eliminating the need for users to remember specific points while maintaining security verification through automated feature extraction and comparison.
2Reliability
If conventional systems use static picture-based authentication, then security proof is generated, but real-time environmental recognition capability is lost
Solution Approach 1:
The system transitions from static picture-based authentication to dynamic real-time environmental recognition. Images are captured and processed in real-time, allowing the security proof to be generated based on the current environment rather than a predetermined static image, thereby achieving both security verification and real-time adaptability.
3Difficulty of detecting and measuring
If conventional systems map identified objects with real-time environment scenes, then object recognition is performed, but mapping accuracy deteriorates due to erroneous results
Solution Approach 1:
The system captures real-time images of the environment, extracts features, compares them with stored features, and generates a confidence score based on the similarity match. This feedback mechanism ensures accurate mapping by continuously verifying environmental features against the stored security proof, eliminating erroneous results through systematic comparison and validation.
Data Source
AI summary
System and method to generate a unique security proof for secure accessing of data are disclosed. The method includes acquiring at least one image of a user's environment, analysing the at least one image for obtaining a first set of features, generating a unique security proof comprising a pair of keys, transmitting the unique security proof a key storage unit for storing the pair of keys, receiving at least one real-time image of the user's environment in real time, extracting a second set of features of the corresponding at least one real-time image, comparing the second set of features with the first set of features, determining one or more similarities between the second set of features and the first set of features, generating a confidence score, generating a notification for enabling the use of the pair of keys based on the confidence score generated for securely accessing of the data.