Secure Credential Fraud Detection via Texture Segmentation
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Solution Overview
Problem
The increasing use of mobile devices has led to rising rates of identity theft and fraud, particularly in remotely authenticating secure credentials, as existing methods struggle to accurately detect fraudulently generated or photocopied secure credential documents.
Innovation Solution
A method and system utilizing texture analysis, where an image of a secure credential document is segmented into regions, and local high-resolution texture features are extracted using machine learning models to generate a score indicating the likelihood of fraud, incorporating techniques such as local binary pattern (LBP) features and histogram normalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional authentication methods are used to verify secure credentials, then the process is simple and fast, but the detection accuracy of fraudulently generated or photocopied documents is insufficient
Solution Approach 1:
The patent segments the secure credential document into multiple regions and extracts texture features from each region independently. This segmentation approach enables detailed local analysis of texture patterns while maintaining an organized processing structure, thereby improving detection accuracy without overwhelming system complexity
Solution Approach 2:
The patent introduces texture analysis as an intermediary layer between the authentication system and the credential document. By analyzing texture features (such as paper texture, print patterns, and material characteristics) as an intermediate step, the system can detect fraudulent documents with high accuracy while keeping the overall authentication framework manageable
2Ease of operation
If remote authentication using mobile devices is implemented, then accessibility and convenience are improved, but the risk of identity theft and fraud increases
Solution Approach 1:
The patent replaces physical inspection methods with automated texture analysis using machine learning models. The system captures images via mobile device cameras and automatically analyzes texture features through computational algorithms, maintaining remote authentication convenience while significantly reducing fraud risk through objective, data-driven detection
Solution Approach 2:
The patent implements a feedback mechanism where the machine learning model continuously evaluates texture features and provides authentication decisions. The system learns from training data and can adapt to new fraud patterns, creating a closed-loop system that maintains high security while preserving remote authentication accessibility
3Measurement precision
If detailed texture analysis is performed on credential documents, then detection precision is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing the credential document image (such as converting to grayscale, normalizing, and segmenting) before the main texture analysis. This preliminary preparation organizes the data in advance, enabling faster and more efficient texture feature extraction during the actual detection process
Solution Approach 2:
The patent divides the credential document into multiple regions and extracts texture features from each region separately. This segmentation allows parallel processing of different areas, reducing overall processing time while maintaining detailed analysis precision for each segment
Data Source
AI summary
A method for detecting images of fraudulently generated or photocopied secure credential documents using texture analysis includes receiving, by one or more processors, an image of a secure credential document from a computer device. The one or more processors segment the image of the secure credential document into multiple regions. For each region of the multiple regions, the one or more processors extract local high-resolution texture features from the image of the secure credential document. The one or more processors generate a score based on the local high-resolution texture features using a machine learning model. The score is indicative of a likelihood that the image of the secure credential document is fraudulently generated or photocopied. The one or more processors transmit a message to a display device indicating that the image of the secure credential document is fraudulently generated or photocopied.