Neural Style Transfer Slider Puzzle CAPTCHA
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
As object recognition tools improve, traditional CAPTCHA systems become increasingly vulnerable to automated attacks, making it difficult to distinguish human input from machine input effectively.
Innovation Solution
The implementation of a CAPTCHA system that utilizes neural style transfer to create images with shape biases, making it harder for automated tools to identify the original image, combined with a slider puzzle mechanism that requires human interaction to correctly place a missing block, thereby enhancing security against automated attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional object recognition CAPTCHA is used, then the system is simple and easy to implement, but it becomes vulnerable to automated attacks as object recognition tools improve
Solution Approach 1:
The patent applies neural style transfer to transform the original CAPTCHA image into a style-transferred version, changing the texture and visual parameters while preserving the underlying shape information. This transformation makes the CAPTCHA resistant to automated object recognition tools that rely on texture analysis, thereby improving security without fundamentally changing the system architecture
Solution Approach 2:
The patent divides the CAPTCHA image into multiple blocks, where only certain blocks contain the actual puzzle elements (hollow blocks and missing block). This segmentation forces automated tools to analyze multiple regions and increases the complexity of automated solving, while the overall system remains a standard CAPTCHA interface
2Reliability
If neural style transfer is applied to the CAPTCHA image, then automated recognition becomes more difficult, but the image processing complexity increases
Solution Approach 1:
The neural style transfer is pre-applied to generate the CAPTCHA image before it is presented to the user. This preliminary processing ensures that the image is already in its protected form, eliminating the need for real-time processing during the CAPTCHA challenge and simplifying the user interaction flow
3Reliability
If multiple hollow blocks are used in the CAPTCHA, then the probability of successful automated guessing decreases, but the puzzle complexity increases
Solution Approach 1:
The patent uses asymmetric distribution of hollow blocks within the CAPTCHA image, where the positions and configurations of hollow blocks are strategically placed to create a unique puzzle pattern. This asymmetry makes it difficult for automated tools to predict or guess the correct configuration, while the underlying structure remains a simple grid-based puzzle
Data Source
AI summary
Two CAPTCHA variants based on neural style transferred image are described: an option-based CAPTCHA and a slider-based CAPTCHA. In the neural style transfer-based slider puzzle CAPTCHA, a neural style transferred image is used as the background. Multiple missing blocks and a puzzle block to be moved are embedded on the neural style transferred image. The user is presented with a slider, using which they can drag the sliding block and place it on the correct missing block. Since the background image is neural style transferred, it becomes difficult to decipher the original image due to high difference in the texture. Placing multiple missing blocks makes the system more resilient to attacks because the chances of finding the correct missing block position is decreased. In the option-based neural style transfer image CAPTCHA, a neural style transferred image of an object/animal is presented to the user along with multiple options. The user is asked to select the option that best describes the presented image. A neural style transferred image helps the CAPTCHA to be more resilient to automated attacks, since the ability of image being reverse searched and answered is less.