Document Image Forgery Detection Using GAN Training Data
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Solution Overview
Problem
Existing document verification systems are vulnerable to forgery and fraud due to the capabilities of generative AI models, leading to inefficiencies and security risks, particularly in real-time or near real-time computing environments.
Innovation Solution
Implementing a Generative Adversarial Network (GAN)-based model for document forgery detection, utilizing a generator and discriminator neural networks to identify inconsistencies and generate fake documents for training, thereby enhancing the accuracy and efficiency of fraud detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional document verification systems are used, then processing time is reduced, but detection precision of forgeries deteriorates
Solution Approach 1:
The system performs preliminary analysis by training the neural network model on extensive datasets of forged and authentic documents before actual verification. The model pre-learns patterns, features, and inconsistencies associated with various forgery techniques, enabling rapid real-time detection without requiring complex analysis during the verification process itself.
Solution Approach 2:
The patent replaces manual document verification processes with an automated neural network-based system. The neural network automatically analyzes document images, identifies forged elements, and makes verification decisions without human intervention, thereby improving both speed and consistency of forgery detection.
2Productivity
If manual document verification is performed, then measurement precision is improved, but productivity deteriorates
Solution Approach 1:
The neural network system performs self-service by automatically analyzing document images, detecting forgeries, and generating verification results without requiring manual review. The system independently processes multiple documents simultaneously, maintaining high accuracy while dramatically increasing verification throughput compared to manual processes.
Solution Approach 2:
The system creates and analyzes multiple copies or versions of document images through the neural network, examining various features and characteristics simultaneously. This enables comprehensive forgery detection across multiple dimensions without requiring sequential manual inspection of each document.
3Reliability
If advanced forgery detection methods are implemented, then reliability is improved, but device complexity deteriorates
Solution Approach 1:
The neural network model serves multiple functions within a single unified system: it detects various types of forgeries (digital manipulation, photo substitution, document alteration), analyzes different document types (IDs, passports, certificates), and provides both binary verification decisions and detailed analysis results. This multi-functionality reduces the need for separate specialized systems for different verification tasks.
Data Source
AI summary
There are provided systems and methods for document image forgery and integration detection using generative artificial intelligence. A service provider, such as an electronic transaction processor for digital transactions, may provide computing services to users, which may be used to engage in interactions with other users and entities including for electronic transaction processing. When utilizing these services, document verification may be required to verify a document. A document may be submitted for document verification, which may be analyzed to determine if the document is forged. To train a machine learning model for document forgery detection a generative adversarial network may be used to generate fake documents of forgeries based on trends in forgeries of real documents. These fake documents may be provided as additional training data to more robustly train a model and keep up on changes in forgery techniques.