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DOCUMENT IMAGE FORGERY AND INTEGRITY DETECTION USING GENERATIVE ARTIFICIAL INTELLIGENCE

发明专利审中
20权利要求 · 3 独立
§ Ⅰ

卷宗概要

发明人

Vipul Jain; Sreeram Vasudevan; Li Hua Lim; Tamil Mani Arul

IPC 分类

G6V 20/G6F 21/60G6V 10/774G6V 10/776G6V 10/82G6V 30/10G6V 30/414G6V 30/418

CPC 分类

G6V20/95G6F21/602G6V10/774G6V10/776G6V10/82G6V30/10G6V30/414G6V30/418G6V2201/10

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.

原文(中文)

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.