Document Generator Identification via Color Footprint Analysis
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Solution Overview
Problem
Automated expense management systems face challenges in accurately identifying fraudulent receipts generated by suspicious document generators, leading to potential revenue loss and decreased system accuracy.
Innovation Solution
A machine learning-based approach that uses color footprints to differentiate between valid and fraudulent receipts by analyzing the most frequently occurring color values in training documents and comparing them to a document generator footprint, thereby classifying documents as either generated-by-the-document-generator or not-generated-by-the-document-generator.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated expense management systems use traditional validation methods, then processing speed is maintained, but accuracy in identifying fraudulent receipts deteriorates
Solution Approach 1:
The patent transforms the receipt validation problem from traditional text-based verification to color-based analysis. By extracting and comparing color histograms (footprints) from receipt images, the system creates a new parameter space for fraud detection. This parameter transformation enables the system to identify fraudulent receipts through color pattern matching while maintaining efficient automated processing speeds.
2Measurement precision
If the system implements comprehensive fraud detection analysis, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent extracts only the essential color information from receipt images by generating color histograms, discarding other complex visual data. This extraction approach focuses the fraud detection system on the most discriminative feature (color distribution patterns) while avoiding the complexity of analyzing entire image contents, thereby achieving high detection accuracy with relatively simple system architecture.
3Measurement precision
If the system processes more detailed document features, then classification accuracy improves, but loss of time increases
Solution Approach 1:
The patent applies partial action by analyzing only the color histogram features of receipts rather than performing comprehensive document analysis. This selective approach processes a subset of relevant features (color distributions) that are sufficient for fraud detection, avoiding the time cost of analyzing all possible document characteristics while maintaining effective classification accuracy.
Data Source
AI summary
The present disclosure involves systems, software, and computer implemented methods for identifying document generators by color footprints. An example method includes receiving a request to classify a first document. A document footprint is generated for the first document that includes a set of most frequently occurring color values in the first document. A classification for the first document is determined as either generated-by-the-document-generator or not-generated-by-the-document-generator based on comparing the document footprint for the first document to a document generator footprint. The document generator footprint includes a set of common color values that occur in a set of training documents for the document generator. The classification for the first document is provided in response to the request.