Document Blur Inconsistency Detection for Fraud Verification
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Solution Overview
Problem
Existing document verification systems struggle to effectively detect and mitigate fraudulent documents, particularly those manipulated using photo editing software, which can lead to identity theft and other security breaches.
Innovation Solution
A document evaluator system that uses optical character recognition and object detection to assess blur inconsistencies within a document, determining a measure of blur for different portions and modifying the acceptance likelihood based on the presence of inconsistencies, thereby identifying potential fraud.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If blur analysis is applied to detect fraudulent documents, then detection accuracy improves, but processing time increases
Solution Approach 1:
The document is divided into multiple portions or regions, and blur analysis is performed on each segment independently. This allows the system to process only relevant areas rather than the entire document, improving detection accuracy while reducing overall processing time through parallel computation of segmented regions.
2Measurement precision
If multiple blur measurement techniques are used, then measurement precision improves, but device complexity increases
Solution Approach 1:
Multiple blur measurement techniques (such as Canny edge detection, Laplacian variance, and Cepstral techniques) are merged into a single integrated analysis framework. The system combines these different methods to compute comprehensive blur values, achieving higher measurement precision while managing complexity through unified processing architecture.
Data Source
AI summary
The disclosure includes a system and method for determining a first measure of blur value associated with a first portion of a document under test; determining a second measure of blur value associated with a second portion of the document under test; determining whether an inconsistency in a set measure of blur values associated with the document under test is present, wherein the set of measure of blur values associated with the document under test includes the first measure of blur value and the second measure of blur value; and modifying a likelihood that the document is accepted or rejected based on whether the inconsistency is absent or present, respectively.