AI Image Comparison for Component Fraud Detection
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Solution Overview
Problem
Conventional fraud detection methods in enterprises, which rely on labor-intensive visual inspection by human agents, are prone to errors and result in significant resource losses due to the return of incorrect or fraudulent items.
Innovation Solution
Implementing security-related image processing using artificial intelligence techniques, specifically through a computer vision model, to compare user-provided component images with reference images, and automate fraud detection by generating similarity scores and initiating appropriate actions based on the comparison results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If visual inspection by human agents is used for fraud detection, then the process can identify fraudulent items, but the process becomes labor-intensive and error-prone
Solution Approach 1:
The patent replaces the mechanical system of human visual inspection with an automated image processing system using computer vision models. The system captures images of returned components, extracts features, compares them with reference images, and automatically determines authenticity without human intervention, thereby eliminating labor-intensive operations while maintaining detection capability
Solution Approach 2:
The system enables self-service fraud detection by automatically processing component images through multiple analysis stages including feature extraction, comparison with reference data, and authenticity determination. The automated system serves itself by performing all detection functions without requiring human agents to manually inspect each component
2Reliability
If human agents perform visual inspection of returned items, then fraud can be detected, but significant resource losses occur due to labor requirements
Solution Approach 1:
The patent substitutes human labor with an automated computer vision system that processes component images through feature extraction, comparison, and analysis stages. This automated system dramatically reduces resource consumption by eliminating the need for human agents to manually inspect each returned component while maintaining reliable fraud detection capability
3Reliability
If conventional visual inspection methods are used, then fraud detection can be performed, but the process is time-consuming and reduces productivity
Solution Approach 1:
The system performs preliminary actions by pre-storing reference images of authentic components and pre-configuring multiple computer vision models for different analysis stages. When a component is returned, the system immediately retrieves reference data and begins automated image processing without delay, significantly accelerating the verification process compared to manual inspection
Solution Approach 2:
The automated image processing system replaces slow manual visual inspection with rapid computer-based analysis. The system captures images, extracts features, compares them with reference data, and determines authenticity automatically in a fraction of the time required for human inspection, thereby dramatically improving processing speed and productivity
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for security-related image processing using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining image data associated with at least one user-provided component; obtaining identifier data associated with the at least one user-provided component; obtaining image data associated with at least one reference component from at least one database using at least a portion of the obtained identifier data; performing a comparison, using at least one pretrained computer vision model, of at least a portion of the obtained image data associated with the at least one user-provided component and at least a portion of the obtained image data associated with the at least one reference component; and performing one or more security-related actions based at least in part on results of the comparison.