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. 18, 2024

Filed:

Feb. 26, 2021
Applicant:

Robert Bosch Gmbh, Stuttgart, DE;

Inventors:

Robin Hutmacher, Renningen, DE;

Jan Hendrik Metzen, Boeblingen, DE;

Nicole Ying Finnie, Renningen, DE;

Assignee:

ROBERT BOSCH GMBH, Stuttgart, DE;

Attorneys:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 21/64 (2013.01); G06N 3/04 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01); G06N 3/088 (2023.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 20/56 (2022.01); G06V 20/58 (2022.01);
U.S. Cl.
CPC ...
G06N 3/088 (2013.01); G06F 21/64 (2013.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 20/56 (2022.01); G06V 20/582 (2022.01);
Abstract

A method for measuring the sensitivity of a classifier for digital images against adversarial attacks. The classifier includes at least one neural network. The method includes: providing a digital image for which the sensitivity is to be measured; providing a generator that is trained to map elements of a latent space to a realistic image; obtaining, according to a set of parameters, an element of the latent space; mapping, using the generator, this element to a disturbance in the space of realistic images; perturbing the digital image with this disturbance; determining, using the classifier, a classification result for the perturbed image; determining, from the classification result, the impact of the disturbance on the classification result; optimizing the set of parameters to maximize this impact; and determining, based at least in part on the maximum impact, the sensitivity of the classifier.


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