Vehicle Facial Authentication Using Blockchain Against Camera Spoofing
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Solution Overview
Problem
Current facial recognition systems in vehicles are vulnerable to spoofing, where unauthorized access can occur using photographs or altered appearances, and are also affected by sensor functionality issues such as weather conditions and camera health.
Innovation Solution
Implementing a system that uses a permissioned blockchain to validate users through a consensus among predefined nodes, which includes detecting the user with exterior sensors, performing facial recognition, and requesting higher levels of authentication such as voice recognition, biometric evaluations, or thermal imaging if initial authentication fails, to prevent spoofing and ensure secure vehicle access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If facial recognition system is used for vehicle access, then access convenience is improved, but security against spoofing deteriorates
Solution Approach 1:
The authentication system is segmented into multiple independent verification nodes (camera sensor, liveness detection module, blockchain validation nodes) that each perform specific authentication tasks. This segmentation prevents spoofing by requiring multiple separate verification steps rather than a single point of failure.
Solution Approach 2:
A blockchain-based consensus mechanism acts as an intermediary between the facial recognition system and vehicle access control. The blockchain network validates authentication results through multiple predefined nodes, providing an additional layer of verification that prevents spoofing while maintaining access convenience.
2Reliability
If multiple authentication levels are implemented, then security against spoofing is improved, but system complexity deteriorates
Solution Approach 1:
The blockchain consensus mechanism serves multiple functions simultaneously: it validates authentication results, prevents spoofing through distributed verification, and provides a secure access control interface. This multi-functionality reduces overall system complexity by consolidating multiple security functions into a single framework.
Solution Approach 2:
The system dynamically adjusts authentication parameters based on detected conditions. When spoofing is detected or weather conditions affect sensor functionality, the system automatically increases authentication requirements (e.g., requiring additional blockchain node confirmations), thereby adapting security levels without permanent system complexity increases.
3Reliability
If blockchain consensus validation is used, then authentication reliability is improved, but processing time deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-configuring multiple predefined blockchain nodes and establishing consensus rules before authentication is needed. This preparation allows for faster real-time validation during actual authentication events, reducing processing time while maintaining high reliability through pre-established verification pathways.
Data Source
AI summary
The disclosure is generally directed to systems and methods for detecting presence of a potential user of a vehicle, validating the potential user with a permissioned blockchain with a plurality of predefined nodes through a consensus among the predefined nodes, determining a level of authentication for the potential user according to the consensus and allowing the potential user to enter the vehicle and denying access to predetermined vehicle systems if the consensus fails to provide the level of authentication above a predefined percentage. The initializing the authentication based on detected presence of the potential user includes detecting the potential user with an exterior sensor coupled to the vehicle and applying the permissioned blockchain to perform facial recognition including performing an initial determination of authentication and requesting confirmation of the initial determination from at least one of the plurality of 63 predefined nodes.