Dynamic Video CAPTCHA Generation for Bot Security
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing CAPTCHAs are vulnerable to decoding by advanced Optical Character Recognition (OCR) techniques and static logic-based systems, allowing bots to bypass human interaction verification.
Innovation Solution
A dynamic challenge generation method that selects a set of objects with defined properties, generates queries based on these properties, removes ambiguous queries, and presents a randomly selected query to users, making it difficult for computer programs to decode.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional CAPTCHA methods (distorted text images, OCR-based challenges) are used, then implementation simplicity is maintained, but security against automated bots deteriorates as OCR techniques advance
Solution Approach 1:
The patent transitions from static CAPTCHA images to dynamic video-based challenges. The video content changes continuously through camera capture and real-time processing, making it impossible for bots to decode all variations in advance. The system dynamically generates challenges by capturing video segments and processing them through AI analysis rather than relying on fixed image patterns.
Solution Approach 2:
The patent changes the fundamental parameters of the CAPTCHA from static text/images to dynamic video sequences with multiple varying properties. Video parameters such as motion patterns, temporal sequences, and contextual relationships are used to create challenges that require understanding rather than simple recognition, fundamentally changing the nature of the verification task.
2Device complexity
If static logic-based CAPTCHAs are used, then decoding by bots becomes possible once logic is identified, but implementation complexity is reduced
Solution Approach 1:
The system replaces static logic with dynamic AI-based analysis. Instead of predetermined rules that bots can reverse-engineer, the system uses machine learning models to analyze video content in real-time. The challenges are generated and evaluated dynamically, preventing bots from pre-computing solutions based on static patterns.
Solution Approach 2:
The patent introduces an AI processing layer as an intermediary between the video input and the verification decision. This AI mediator analyzes complex video content including object recognition, motion analysis, and contextual understanding, creating a barrier that traditional bot decoding methods cannot penetrate. The AI intermediary transforms the verification process from rule-based to intelligence-based.
3Ease of manufacture
If OCR-decodable text images are used, then ease of implementation is maintained, but vulnerability to automated recognition increases
Solution Approach 1:
The patent extracts the verification task from text/image recognition and relocates it to video-based contextual understanding. Instead of presenting text that can be extracted and decoded by OCR, the system presents video content requiring comprehension of scenes, objects, and relationships. The harmful OCR capability is rendered ineffective by removing text-based challenges entirely.
Solution Approach 2:
The patent substitutes the mechanical/optical recognition system (OCR) with an AI-based video analysis system. Instead of relying on character pattern recognition, the system uses machine learning to understand video content, replacing brittle text-based verification with robust visual-intelligence-based verification that cannot be decoded by traditional OCR methods.
Data Source
AI summary
The invention provides a method, a system, and a computer program product checking for human interaction dynamically to enable access to a resource in a computing environment. The method comprises collecting a plurality of objects. For each object, a plurality of properties is defined. A set of objects is selected from the plurality of collected objects. Thereafter, queries are generated based on the properties of the selected objects. Queries which have a non-unique or ambiguous response are removed. One of the remaining queries is randomly selected. The answer to the query, based on properties of the selected objects, is stored. The selected set of objects and the selected query are presented to the user who is trying to gain access to the resource. The user is enabled access to the resource if the response received from the user is validated against the stored answer of the selected query.