Multimedia Authentication via Deep Content Classification
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Solution Overview
Problem
Current user verification methods for mobile devices, such as passcodes and fingerprint recognition, are insecure and unreliable due to vulnerabilities in password protection and ease of duplication or simulation, necessitating a more robust authentication solution.
Innovation Solution
A system utilizing a deep-content-classification (DCC) system that generates and matches multimedia identification signals, including images and audio, to create concept structures for secure user verification, eliminating the need for traditional passcodes or fingerprints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional passcodes or fingerprints are used for user verification, then the authentication process is simple and quick, but the security reliability is low due to vulnerabilities in password protection and ease of duplication or simulation
Solution Approach 1:
The patent combines multiple identification parameters (images, audio signals, and other multimedia content) into a unified authentication system. By merging these diverse biometric data types, the system achieves higher security reliability while maintaining a user-friendly interface, resolving the contradiction between reliability and complexity.
Solution Approach 2:
The authentication system uses composite biometric data structures that integrate multiple types of identification signals (visual images, audio recordings, and multimedia content). This composite approach enhances security reliability by making duplication and simulation significantly more difficult, while the system manages complexity through integrated processing algorithms.
2Reliability
If multiple identification parameters are used for user verification, then the security is enhanced, but the number of comparisons needed increases, reducing processing efficiency
Solution Approach 1:
The system performs preliminary processing of multimedia identification signals during the authentication setup phase. User images, audio samples, and other biometric data are pre-analyzed and stored as reference templates. During actual verification, the system compares new inputs against these pre-prepared templates, significantly reducing processing time while maintaining high security through multiple parameter verification.
3Measurement precision
If deep-content-classification system is used to match multimedia identification signals, then the accuracy of user verification is improved, but the computational resources and system complexity increase
Solution Approach 1:
The deep-content-classification system is divided into modular functional components that process different types of multimedia data separately (image processing module, audio processing module, multimedia analysis module). Each module handles specific identification parameters independently, improving verification accuracy through specialized processing while reducing overall system complexity through modular architecture and independent optimization of each component.
Data Source
AI summary
A method and system for verifying an identity of a user accessing a user device are provided. The method includes receiving a request to verify the user identity accessing the user device; receiving a plurality of multimedia identification signals from the user device; querying a deep-content-classification (DCC) system to find a match between at least one concept structure associated with the user and the plurality of received multimedia identification signals, wherein the concept structure is created during a sign-in procedure; and sending an authentication notification upon determination of a match between the at least one concept structure and the plurality of received multimedia identification signals.