Dynamic CAPTCHA Generation via Text-to-Image AI
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Solution Overview
Problem
Existing CAPTCHA systems rely on static image datasets or annotated images, which are not dynamically generated and personalized, making them vulnerable to brute-force attacks and less effective in differentiating human users from automated bots.
Innovation Solution
The proposed solution involves a policy-compliant CAPTCHA generation system that utilizes a combination of Large Language Model (LLM), Text-to-Image generation AI models, and Neural Style Transfer (NST) to dynamically and personally generate CAPTCHAs from text, creating secure and personalized image CAPTCHAs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static image datasets or annotated images are used for CAPTCHA generation, then the system is simpler to implement, but the CAPTCHA becomes vulnerable to brute-force attacks and less effective at differentiating human users from automated bots
Solution Approach 1:
The patent applies dynamics by transitioning from static image datasets to dynamic text-to-image generation. The system generates CAPTCHA images on-demand using AI models, making each CAPTCHA unique and unpredictable. This dynamic generation process prevents brute-force attacks by ensuring that previously seen images or patterns cannot be reused, thereby significantly improving security while managing complexity through automated generation.
Solution Approach 2:
The system implements self-service through automated text-to-image generation where the CAPTCHA images are created autonomously based on textual prompts. The AI model generates appropriate images without requiring manual annotation or pre-existing datasets, enabling the system to serve itself by generating secure, personalized CAPTCHAs on-demand. This self-service approach improves security while reducing dependency on complex manual curation processes.
2Reliability
If dynamically generated personalized CAPTCHAs are created using AI models, then the security and effectiveness improve, but the computational resources and generation time increase
Solution Approach 1:
The patent applies partial action by generating only the necessary portion of the CAPTCHA image based on the textual prompt, rather than generating complete images from scratch every time. The system uses text-to-image AI models to create personalized CAPTCHAs with just the required level of detail and complexity needed for security, avoiding unnecessary computational overhead while maintaining effectiveness against automated bots.
Solution Approach 2:
The system implements parameter changes by adjusting the generation parameters of the AI model based on the specific CAPTCHA requirements. Instead of using fixed generation settings, the system dynamically modifies parameters such as image resolution, complexity level, and style based on the textual prompt and security needs. This allows the system to optimize computational energy usage by reducing generation complexity when lower security levels are sufficient while increasing resources only when higher effectiveness is required.
3Adaptability or versatility
If CAPTCHAs are generated from text using AI models, then personalization and security enhance, but the difficulty of detecting and measuring CAPTCHA characteristics increases
Solution Approach 1:
The patent applies mechanics substitution by replacing traditional mechanical CAPTCHA design approaches with AI-based text-to-image generation. Instead of manually designing CAPTCHAs with specific detectable characteristics, the system uses neural networks to generate images from text prompts. This substitution makes it extremely difficult for attackers to predict or analyze CAPTCHA characteristics, as the generation process is based on complex semantic understanding rather than predictable patterns, thereby enhancing personalization and security.
Solution Approach 2:
The system uses an intermediary approach where text prompts serve as the intermediate representation between the user's input and the final CAPTCHA image. The text-to-image AI model acts as a mediator that translates semantic meanings into visual representations, creating CAPTCHAs that are personalized and secure. This intermediary process makes it difficult for attackers to directly analyze or predict CAPTCHA characteristics, as they would need to understand both the text semantics and the image generation transformation.
Data Source
AI summary
Disclosed is a system and method to generate policy compliant CAPTCHA images to be rendered on a CAPTCHA question using an image composer, which takes a policy compliant sentence generated by a sentence composer. The policy considered to validate the CAPTCHA is one or combination of privacy policy, client-specific configuration like GDPR, HIPAA, etc. The CAPTCHA images are generated by the image composer based on the sentence generated by the sentence composer and structured knowledge graph.