CAPTCHA Verification Data Processing via User Segmentation
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Solution Overview
Problem
Existing CAPTCHA verification methods lack accuracy in estimating cracking resistance and difficulty, as they rely on simple and rough calculations of passing rates, which are not effective in distinguishing between human and automated attempts.
Innovation Solution
A verification data processing method and device that utilizes a server to receive CAPTCHA response information, determine user types based on IP addresses and verification data, and store this information in a hash table and min-heap structure to calculate precise passing rates for each CAPTCHA code type and user type.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a simple total passing rate calculation method is used, then the calculation process is simple and fast, but the accuracy of CAPTCHA verification assessment is low
Solution Approach 1:
The patent segments verification data by user type (human user, code worker, automation) and CAPTCHA code type, creating separate statistical categories. This segmentation allows for more precise passing rate calculations within each category while maintaining manageable processing complexity through structured data organization using hash tables and min-heaps.
Solution Approach 2:
The patent changes the parameters of passing rate calculation from a single aggregate metric to multiple differentiated metrics based on user type and CAPTCHA code type. By introducing these additional classification parameters, the system achieves higher measurement precision without proportionally increasing complexity, as the framework efficiently handles multi-dimensional data.
2Reliability
If verification data is collected and processed in detail, then the accuracy of cracking resistance estimation is improved, but the data processing complexity increases
Solution Approach 1:
The patent performs preliminary classification and organization of verification data using hash tables keyed by IP address and CAPTCHA code type. This preliminary action structures the data before detailed processing, enabling efficient retrieval and analysis that maintains reliability while controlling complexity through pre-organized data structures.
Solution Approach 2:
The patent creates simplified representations of verification data by storing aggregated statistics (total verification counts, correct verification counts) in hash tables and min-heaps rather than processing all raw verification records. This copying approach maintains the essential information needed for reliable cracking resistance estimation while significantly reducing processing complexity.
3Productivity
If a single aggregate passing rate is calculated, then the calculation is fast and simple, but the result has low value in estimating cracking difficulty
Solution Approach 1:
The patent applies local quality by calculating passing rates separately for different user types and CAPTCHA code types rather than using a single aggregate metric. This allows each category to have its own optimized passing rate calculation that maintains calculation speed while preserving important information about cracking difficulty specific to each category.
Solution Approach 2:
The patent adds dimensions to the passing rate calculation by introducing user type and CAPTCHA code type as classification axes. This transforms the calculation from a single-dimensional aggregate metric to a multi-dimensional analysis framework, preserving rich information about cracking resistance while maintaining efficient calculation through structured data organization.
Data Source
AI summary
A verification data processing method includes: receiving CAPTCHA response information sent by user through a user terminal and obtaining an IP address of the user terminal from the CAPTCHA response information; obtaining verification data by verifying the CAPTCHA response information, and storing the verification data in a preset hash table, where the verification data includes the type of a CAPTCHA code corresponding to the CAPTCHA response information, and the number of total verifications of CAPTCHA response information and the number of correct verifications both corresponding to the IP address; determining a user type of the user according to the IP address, the number of total verifications and the number of correct verifications; storing the verification data in a min-heap according to the CAPTCHA code type and the user type; and making a statistic of all the numbers of total verifications and all the numbers of correct verifications in the min-heap.