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:
Jun. 27, 2023

Filed:

May. 21, 2021
Applicant:

GE Precision Healthcare Llc, Wauwatosa, WI (US);

Inventors:

Ravi Soni, San Ramon, CA (US);

Min Zhang, San Ramon, CA (US);

Zili Ma, San Ramon, CA (US);

Gopal B. Avinash, San Ramon, CA (US);

Assignee:

GE PRECISION HEALTHCARE LLC, Wauwatosa, WI (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06V 10/82 (2022.01); G06V 10/772 (2022.01); G16H 50/20 (2018.01); G06N 3/08 (2023.01); G16H 30/40 (2018.01); G06V 10/774 (2022.01); G06V 10/778 (2022.01);
U.S. Cl.
CPC ...
G16H 50/20 (2018.01); G06N 3/08 (2013.01); G06T 7/0012 (2013.01); G06V 10/772 (2022.01); G06V 10/774 (2022.01); G06V 10/7784 (2022.01); G06V 10/82 (2022.01); G16H 30/40 (2018.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20092 (2013.01);
Abstract

Techniques are provided for deep neural network (DNN) identification of realistic synthetic images generated using a generative adversarial network (GAN). According to an embodiment, a system is described that can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise, a first extraction component that extracts a subset of synthetic images classified as non-real like as opposed to real-like, wherein the subset of synthetic images were generated using a GAN model. The computer executable components can further comprise a training component that employs the subset of synthetic images and real images to train a DNN network model to classify synthetic images generated using the GAN model as either real-like or non-real like.


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