Invention Grant
- Patent Title: Deep learning stack used in production to prevent exfiltration of image-borne identification documents
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Application No.: US17229768Application Date: 2021-04-13
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Publication No.: US11574151B2Publication Date: 2023-02-07
- Inventor: Xiaolin Wang , Krishna Narayanaswamy , Yi Zhang , Siying Yang
- Applicant: Netskope, Inc.
- Applicant Address: US CA Santa Clara
- Assignee: Netskope, Inc.
- Current Assignee: Netskope, Inc.
- Current Assignee Address: US CA Santa Clara
- Agency: Haynes Beffel & Wolfeld, LLP
- Agent Ernest J. Beffel, Jr.
- Main IPC: G06K9/62
- IPC: G06K9/62 ; G06N5/04 ; G06N3/08 ; G06V30/19 ; G06V10/82 ; G06V30/40 ; G06V30/10 ; G06N3/04

Abstract:
Disclosed is detecting identification documents in image-borne identification documents and protecting against loss of the image-borne identification documents. A trained deep learning (DL) stack is used to classify production images by inference as containing a sensitive image-borne identification document, with the trained stack configured with parameters determined using labelled ground truth data for the identification documents and examples of other image documents. The trained DL stack is configured to include a first set of layers closer to an input layer and a second set of layers further from the input layer, with the first set pre-trained to perform image recognition before exposing the second set of layers of the stack to the labelled ground truth data for the image-borne identification documents and examples of other image documents, and using the inferred classification of the sensitive image-borne identification document in a DLP system to protect against loss by image exfiltration.
Public/Granted literature
- US20210383159A1 DEEP LEARNING STACK USED IN PRODUCTION TO PREVENT EXFILTRATION OF IMAGE-BORNE IDENTIFICATION DOCUMENTS Public/Granted day:2021-12-09
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