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US Patent 12067493 Training and configuration of DL stack to detect attempted exfiltration of sensitive screenshot-borne data

Patent 12067493 was granted and assigned to Netskope on August, 2024 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent
0

Patent attributes

Patent Applicant
Netskope
Netskope
0
Current Assignee
Netskope
Netskope
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
120674930
Patent Inventor Names
Siying Yang0
Xiaolin Wang0
Krishna Narayanaswamy0
Yi Zhang0
Date of Patent
August 20, 2024
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Patent Application Number
172020750
Date Filed
March 15, 2021
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Patent Citations
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US Patent 8544060 Method and system for detecting and protecting against potential data loss from unknown applications
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US Patent 8844059 Method and apparatus for preventing data loss through screen capture
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US Patent 8943546 Method and system for detecting and protecting against potential data loss from unknown applications
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US Patent 9137131 Network traffic monitoring system and method to redirect network traffic through a network intermediary
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US Patent 10068071 Screen shot marking and identification for device security
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US Patent 10757090 Secure application access system
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US Patent 10855671 Secure application access system
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US Patent 10867073 Detecting organization image-borne sensitive documents and protecting against loss of the sensitive documents
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...
Patent Primary Examiner
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Jose L Couso
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CPC Code
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H04L 63/105
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H04L 63/20
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H04L 63/04
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G06F 21/554
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G06F 21/6245
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G06F 21/60
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G06F 21/55
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G06F 21/50
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Patent abstract

Disclosed are methods and systems for detecting screenshot images and protecting against loss of sensitive screenshot-borne data. One disclosed method includes collecting examples of the screenshot images and non-screenshot images and creating labelled ground-truth data for the examples. The method also includes applying re-rendering of at least some of the collected example screenshot images to represent different variations of screenshots that may contain sensitive information, and further includes training a deep learning stack by forward inference and back propagation using labelled ground-truth data for the screenshot images and the examples of the non-screenshot images. The method further includes using results of the back propagation to configure parameters of the trained DL stack for inference from images in production. Also disclosed is applying a screenshot robot to collect the examples of the screenshot images and non-screenshot images.

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