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:
May. 30, 2023

Filed:

Jun. 24, 2020
Applicants:

Insurance Services Office, Inc., Jersey City, NJ (US);

The Regents of the University of Colorado, Denver, CO (US);

Inventors:

Aurobrata Ghosh, Pondicherry, IN;

Steve Cruz, Colorado Springs, CO (US);

Terrance E. Boult, Colorado Springs, CO (US);

Maneesh Kumar Singh, Princeton, NJ (US);

Venkata Subbarao Veeravarasapu, Munich, DE;

Zheng Zhong, Seattle, WA (US);

Assignees:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2006.01); G06K 9/62 (2022.01); G06T 7/00 (2017.01); G06N 20/00 (2019.01); G06T 5/00 (2006.01); G06F 18/214 (2023.01);
U.S. Cl.
CPC ...
G06T 7/0002 (2013.01); G06F 18/214 (2023.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01); G06T 5/005 (2013.01);
Abstract

A system for improved localization of image forgery. The system generates a variational information bottleneck objective function and works with input image patches to implement an encoder-decoder architecture. The encoder-decoder architecture controls an information flow between the input image patches and a representation layer. The system utilizes information bottleneck to learn useful residual noise patterns and ignore semantic content present in each input image patch. The system trains a neural network to learn a representation indicative of a statistical fingerprint of a source camera model from each input image patch while excluding semantic content thereof. The system can determine a splicing manipulation localization by the trained neural network.


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