Visual Encoding Recognition Using Context for Fraud Detection
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Solution Overview
Problem
Existing visual encoding systems face challenges in accurately interpreting patterns at varying distances and are susceptible to fraudulent activities due to the inability to verify contextual information, leading to inefficiencies and security vulnerabilities.
Innovation Solution
A computing system employs multiple recognition systems operating on different data signals and processing techniques to process visual encodings, leveraging contextual information such as GPS data, ambient noise, and surrounding visual features to enhance accuracy and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single recognition system is used to process visual encodings, then the device complexity is low, but the recognition accuracy and security are insufficient
Solution Approach 1:
The recognition system is divided into multiple specialized recognition systems, each responsible for processing different aspects of visual encodings. The system segments the recognition task into: (1) a first recognition system for identifying the visual encoding itself, (2) a second recognition system for analyzing surrounding contextual information, and (3) a fraud detection system for verifying authenticity. This segmentation allows each component to specialize in specific functions, improving overall recognition accuracy while maintaining manageable complexity through modular design.
2Reliability
If contextual information verification is added to prevent fraud, then security is improved, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing contextual information before it is needed for verification. The second recognition system continuously analyzes and stores information about surrounding visual elements, environmental context, and encoding patterns in advance. When a visual encoding needs verification, the pre-processed contextual data is already available, allowing rapid fraud detection without significant processing delays. This preliminary action reduces the time penalty associated with security verification.
3Measurement precision
If multiple recognition systems are used to process visual encodings, then recognition accuracy is improved, but the computing resources required increase
Solution Approach 1:
The system applies partial action by selectively activating different recognition systems based on the specific requirements of each visual encoding. Not all recognition systems are activated simultaneously for every encoding - instead, the system activates only the necessary subsystems based on the encoding type, surrounding context, and security requirements. This approach maintains high recognition accuracy when needed while reducing computing resource consumption during routine operations, effectively balancing performance with resource efficiency.
Data Source
AI summary
The present disclosure relates generally to the processing of machine-readable visual encodings in view of contextual information. One embodiment of aspects of the present disclosure comprises obtaining image data descriptive of a scene that includes a machine-readable visual encoding; processing the image data with a first recognition system configured to recognize the machine-readable visual encoding; processing the image data with a second, different recognition system configured to recognize a surrounding portion of the scene that surrounds the machine-readable visual encoding; identifying a stored reference associated with the machine-readable visual encoding based at least in part on one or more first outputs generated by the first recognition system based on the image data and based at least in part on one or more second outputs generated by the second recognition system based on the image data; and performing one or more actions responsive to identification of the stored reference.