View source for 20240005721. SYNTHETIC BANKNOTE DATA GENERATION USING A GENERATIVE ADVERSARIAL NETWORK WITH SPATIALLY COMPOSITED MULTISPECTRAL DATA simplified abstract (NCR Corporation)

=SYNTHETIC BANKNOTE DATA GENERATION USING A GENERATIVE ADVERSARIAL NETWORK WITH SPATIALLY COMPOSITED MULTISPECTRAL DATA=


==Organization Name==

[[:Category:NCR Corporation|NCR Corporation]]


[[Category:NCR Corporation]]

==Inventor(s)==

[[:Category:Alan Greig of Blairgowrie (GB)|Alan Greig of Blairgowrie (GB)]][[Category:Alan Greig of Blairgowrie (GB)]]

==SYNTHETIC BANKNOTE DATA GENERATION USING A GENERATIVE ADVERSARIAL NETWORK WITH SPATIALLY COMPOSITED MULTISPECTRAL DATA - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240005721 titled 'SYNTHETIC BANKNOTE DATA GENERATION USING A GENERATIVE ADVERSARIAL NETWORK WITH SPATIALLY COMPOSITED MULTISPECTRAL DATA

==Simplified Explanation==

The patent application describes a method for creating synthetic banknotes using a generative adversarial network (GAN) trained on a multispectral image of a sample banknote. 

* A multispectral image is generated from a sample banknote.
* The multispectral image is processed to create a training image in a two-dimensional space.
* A generative adversarial network (GAN) is trained using the training image.
* Synthetic banknotes are generated by inputting random data into the trained GAN.
* The synthetic banknotes can be used to create a banknote template for a currency validator.

==Potential Applications==
This technology has potential applications in various fields, including:

* Currency validation systems: The synthetic banknotes can be used to generate templates for currency validators, helping to improve their accuracy and reliability.
* Counterfeit detection: The synthetic banknotes can be used to train machine learning models for counterfeit detection, enhancing the ability to identify fake banknotes.
* Training and education: The synthetic banknotes can be used for training purposes, allowing individuals to practice identifying genuine banknotes without the risk of handling real currency.

==Problems Solved==
The technology addresses several problems related to banknote generation and validation:

* Limited availability of sample banknotes: Generating synthetic banknotes eliminates the need for a large number of real banknotes, which may be difficult to obtain.
* Cost and time efficiency: Creating synthetic banknotes is a faster and more cost-effective method compared to traditional banknote production methods.
* Enhanced accuracy: Training a generative adversarial network on a multispectral image helps to capture the intricate details and features of genuine banknotes, resulting in more accurate validation and counterfeit detection.

==Benefits==
The use of synthetic banknotes and generative adversarial networks offers several benefits:

* Improved validation accuracy: The synthetic banknotes can help currency validators achieve higher accuracy rates in identifying genuine banknotes.
* Cost-effective solution: Generating synthetic banknotes reduces the need for expensive materials and production processes associated with traditional banknote production.
* Enhanced counterfeit detection: The use of synthetic banknotes for training machine learning models can improve the ability to detect counterfeit banknotes, helping to protect against financial fraud.


==Original Abstract Submitted==

a method for generating synthetic banknotes requires that a multispectral image be generated from a sample banknote. the multispectral image is processed to create a training image in a two-dimensional space. a generative adversarial network is trained using the training image. synthetic banknotes are generated by seeding the trained generative adversarial network with random data. the synthetic banknotes may then be used to generate a banknote template for a currency validator.



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