The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Mar. 03, 2026

Filed:

Feb. 28, 2022
Applicant:

Accenture Global Solutions Limited, Dublin, IE;

Inventors:

Changwei Liu, Fairfax, VA (US);

Louis Divalentin, Arlington, VA (US);

Neil Hayden Liberman, Arlington, VA (US);

Amin Hassanzadeh, Arlington, VA (US);

Benjamin Glen Mccarty, Washington, DC (US);

Malek Ben Salem, Falls Church, VA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 21/57 (2013.01); G06N 3/094 (2023.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01);
U.S. Cl.
CPC ...
G06F 21/577 (2013.01); G06N 3/094 (2023.01); G06V 10/7747 (2022.01); G06V 10/776 (2022.01); G06F 2221/033 (2013.01);
Abstract

A device may receive a machine learning model and training data utilized to train the machine learning model, and may perform a data veracity assessment of the training data to identify and remove poisoned data from the training data. The device may perform an adversarial assessment of the machine learning model to generate adversarial attacks and to provide defensive capabilities for the adversarial attacks, and may perform a membership inference assessment of the machine learning model to generate membership inference attacks and to provide secure training data as a defense for the membership inference attacks. The device may perform a model extraction assessment of the machine learning model to identify model extraction vulnerabilities and to provide a secure application programming interface as a defense to the model extraction vulnerabilities, and may perform actions based on results of one or more of the assessments.


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