Contextual Sequential Image Challenges for Bot Attack Prevention
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Solution Overview
Problem
Existing security challenges are easily solvable by bots, leading to security vulnerabilities, as they often rely on simple image processing tasks that machine learning can perform with relative ease, making it difficult to differentiate between humans and automated scripts.
Innovation Solution
A fraud detection service uses machine learning models to generate contextual sequential image completion challenges, where users are asked to identify a sequence of events based on contextually relevant images, while decoy images are generated to confuse bots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If simple image processing tasks are used for security challenges, then ease of operation is improved, but security reliability deteriorates
Solution Approach 1:
The patent changes the parameter of image complexity from simple object recognition to contextual sequential understanding. By requiring users to identify sequences of events across multiple images rather than single objects, the challenge transforms from an easily automatable task to one requiring human-like contextual reasoning, thereby improving security reliability while maintaining operational feasibility
Solution Approach 2:
The patent adds the dimension of temporal sequencing to traditional image recognition challenges. Instead of static single-image analysis, the system presents sequences of images requiring users to understand event progression and contextual relationships across time, creating a more robust security challenge that is difficult for bots to solve
2Reliability
If contextual sequential image completion challenges are used, then security reliability is improved, but device complexity increases
Solution Approach 1:
The system employs machine learning models that can autonomously generate contextual image sequences and evaluate user responses. The ML models self-service the complex tasks of creating coherent event sequences, selecting appropriate decoy images, and verifying user understanding, thereby managing the inherent complexity without requiring manual intervention
Solution Approach 2:
Machine learning models serve as intermediaries between the security system and users. The ML models handle the complex generation and validation of contextual image sequences, acting as a mediator that translates security requirements into manageable challenge formats while maintaining high security standards
3Reliability
If decoy images are generated to confuse bots, then security reliability is improved, but loss of information increases
Solution Approach 1:
The patent applies local quality by making decoy images locally similar to valid sequence images in terms of visual content and context, but globally different in terms of sequence coherence. Each individual image may appear reasonable, but the decoys fail to maintain the correct temporal and contextual relationships when viewed as part of the complete sequence, allowing bots to be confused without overwhelming the system with irrelevant information
Data Source
AI summary
Computer-implemented methods, systems, and devices for generating security challenges are described. An identity management system may obtain image descriptions. The image descriptions may include a first image description set that corresponds to a sequence of events and a second image description set that is unassociated with the sequence of events. The identity management system may obtain images based on the image descriptions. The images may include a first image set that corresponds to the sequence of events and a second image set that is unassociated with the sequence of events. The identity management system may generate a security challenge using the images. The security challenge may request for a user to identify the sequence of events from the images. Identification of the sequence of events may be based on each image of the first image set being contextually relevant to the sequence of events.