Fake Signature Detection Using Inter and Intra-Model Analysis
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Solution Overview
Problem
In remote and electronic transactions, fraudsters exploit image manipulation software to create fake signatures, making it difficult for systems to distinguish between genuine and fraudulent signatures, thereby increasing the risk of successful fraudulent attempts.
Innovation Solution
A system and method for identifying and analyzing signatures using fake signature detection models, including inter-signature and intra-signature detection models, to determine if a signature is fake by identifying commonalities and anomalous consistencies, and taking appropriate actions such as rejecting requests or subjecting them to additional authentication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If image manipulation software is used to create fake signatures, then the ability to forge signatures improves, but the difficulty of detecting fake signatures increases
Solution Approach 1:
The detection system is divided into multiple specialized models: inter-signature detection models that compare signatures across different documents, intra-signature detection models that analyze consistency within a single signature, and font detection models that identify computer-generated text. This segmentation allows each component to specialize in detecting specific types of anomalies, thereby improving overall detection capability against sophisticated forgeries.
Solution Approach 2:
The system moves beyond traditional single-document signature verification by adding the dimension of cross-document analysis. By comparing signatures across multiple documents (inter-signature detection) and analyzing consistency within the signature itself (intra-signature detection), the system creates multiple detection dimensions that make it harder for fraudsters to evade detection through simple image manipulation.
2Measurement precision
If multiple fake signature detection models are applied to analyze signatures, then the accuracy of fake signature detection improves, but the complexity of the detection system increases
Solution Approach 1:
The complex detection task is segmented into multiple specialized models: inter-signature detection models for comparing signatures across documents, intra-signature detection models for analyzing internal consistency, and font detection models for identifying computer-generated text. Each model focuses on specific detection aspects, improving accuracy while managing complexity through functional specialization.
Solution Approach 2:
The detection system employs universal models that can handle multiple detection functions. The inter-signature detection model, for example, serves both to detect forgery and to establish baseline signature characteristics. This multi-functionality reduces overall system complexity by avoiding redundant specialized models for each detection function.
Data Source
AI summary
The disclosure includes a system and method for fake signature detection including identifying, using one or more processors, within a document image a signature portion, the signature portion representing a signature; applying, using the one or more processors, one or more fake signature detection models to the signature portion; determining, using the one or more processors and the one or more fake signature detection models, whether the signature represented in the signature portion is a fake signature; and presenting, using the one or more processors, the determination.