Document Assembly Objects for Faster Fraud-Resistant Verification
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Solution Overview
Problem
Existing document verification systems struggle to efficiently and accurately authenticate documents, particularly in the face of sophisticated fraudulent attempts, such as those using photo editing software, leading to potential identity theft and other security breaches.
Innovation Solution
A document evaluation system that generates a document assembly object comprising document components, derived features, and verification checks, using a document classifier to identify and verify documents based on unique identifiers, aggregated context information, and machine learning to support new documents with minimal data, reducing the 'cold start' problem.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional document verification methods are used, then the system can process documents with existing data, but it fails to accurately detect sophisticated fraudulent documents edited with photo editing software
Solution Approach 1:
The system performs preliminary analysis by generating a document assembly object that includes expected document components, features, and verification checks before actual verification occurs. This preparatory step establishes a framework of what a legitimate document should contain, enabling the system to systematically compare and identify fraudulent alterations in edited documents.
Solution Approach 2:
The verification process is divided into distinct segments: extracting document components (text, images, security features), deriving features from these components, performing verification checks against expected values, and aggregating context information. This segmentation allows the system to methodically examine each element of a document for signs of fraud, improving detection of photo-edited documents.
2Reliability
If the system uses comprehensive verification checks and aggregated context information, then verification accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system pre-generates document assembly objects containing expected components, features, and verification checks for different document types. This preliminary preparation stores verification criteria in advance, so when a document needs verification, the system can quickly retrieve and apply the pre-established framework rather than creating verification rules from scratch, reducing processing time while maintaining comprehensive checks.
Solution Approach 2:
The system dynamically adjusts verification parameters based on the document type and risk level. For low-risk documents with standard formats, the system can use streamlined verification checks. For high-risk or unusual documents, the system activates more comprehensive aggregated context information and verification checks, optimizing the balance between accuracy and processing time.
3Reliability
If the system requires extensive training data for machine learning models, then model accuracy improves, but the cold start problem prevents quick support for new document types
Solution Approach 1:
The system pre-generates document assembly objects that encode expected components, features, and verification checks for various document types based on issuer-provided information and sample documents. This preliminary structuring allows the system to immediately support new document types with minimal training data, as the framework for verification is already established through the document assembly object generation process.
Solution Approach 2:
The document assembly object serves as an intermediary between the machine learning model and the actual document verification. It translates issuer-provided information and sample documents into a structured format containing expected components, features, and verification checks. This intermediary structure allows the system to quickly adapt to new document types by updating the document assembly object template, without requiring extensive retraining of the underlying ML models.
Data Source
AI summary
The disclosure includes a system and method for obtaining a query including a document assembly object identifier, the query associated with a request to verify a document under test present in an image; obtaining a document assembly object describing a valid document uniquely associated with the identifier, the document assembly object including: a set of document components, a set of derived document features, and a set of verification checks including one or more of a direct check and a derived check; obtaining aggregated context information associated with the document under test; and sending the document assembly object and aggregated context information for use in verification of the document under test.