Multi-Factor CAPTCHA With Adjunct Signals for Spoof Resistance
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Solution Overview
Problem
Existing CAPTCHA systems, particularly those using facial identity confirmation, are vulnerable to defeat by techniques such as 2-D Pictures and 3-D photorealistic models, and animatronics, necessitating an additional layer of validation to ensure human access.
Innovation Solution
A multi-zone CAPTCHA system that incorporates video input of user movements and adjunct objects, using processor analysis to validate user interactions and access, including hand and face dimensions, movements, and smartwatch data for enhanced security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If facial identity confirmation is used for CAPTCHA validation, then the ease of operation is improved, but the reliability deteriorates due to vulnerability to 2-D pictures, 3-D photorealistic models, and animatronics
Solution Approach 1:
The patent divides the CAPTCHA validation into multiple independent factors: facial identity confirmation, hand gesture recognition, and adjunct object detection. Each factor operates as a separate validation zone, so that failure or spoofing of one factor does not compromise the entire system. This segmentation maintains ease of operation while improving reliability through multi-factor verification.
Solution Approach 2:
The patent introduces adjunct objects as intermediary elements that mediate between the user and the validation system. These objects serve as additional verification layers that are difficult for automated systems to replicate, thereby improving reliability without significantly increasing operational complexity for legitimate users.
2Reliability
If multiple validation factors are added to overcome false facial identification, then the reliability is improved, but the device complexity increases
Solution Approach 1:
The validation system is segmented into distinct functional zones (facial zone, hand gesture zone, adjunct object zone), each handled by dedicated processing modules. This modular architecture improves reliability through comprehensive verification while managing complexity by organizing validation functions into separate, manageable segments.
Solution Approach 2:
The system uses a single camera input that serves multiple functions: capturing facial identity, hand gestures, and adjunct objects simultaneously. This multi-functionality approach improves reliability through comprehensive validation without proportionally increasing device complexity, as one hardware component performs multiple validation tasks.
3Reliability
If video input with adjunct objects is required for validation, then the reliability is improved, but the loss of time increases due to additional validation steps
Solution Approach 1:
The system processes validation factors in periodic cycles rather than requiring all factors to be captured and validated simultaneously. The camera captures sequential frames that are processed through different validation zones in a time-efficient manner, improving reliability through comprehensive verification while minimizing total validation time through optimized processing sequences.
Solution Approach 2:
The system merges multiple validation tasks into a single unified processing pipeline that analyzes facial identity, hand gestures, and adjunct objects from the same video input stream. This combining approach improves reliability through multi-factor verification while reducing time loss by eliminating the need for separate capture sessions for each validation factor.
Data Source
AI summary
An approach for improving completely automated public turing test to tell computers and humans apart (CAPTCHA) reliability for a resource. The approach receives a request for a CAPTCHA validation from a user. The approach retrieves a CAPTCHA analysis configuration associated with the user. The approach displays a CAPTCHA multi-zone display defined by the configuration. The approach receives video input containing adjunct objects, associated with objects visible on the user, from the user for the multi-zone display. The approach determines if the inputs are valid based on the CAPTCHA analysis configuration. The approach, responsive to the inputs being valid, allows access to the resource.