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:
Nov. 12, 2024

Filed:

Oct. 15, 2021
Applicant:

Pindrop Security, Inc., Atlanta, GA (US);

Inventors:

Tianxiang Chen, Atlanta, GA (US);

Elie Khoury, Atlanta, GA (US);

Assignee:

Pindrop Security, Inc., Atlanta, GA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); G06F 18/21 (2023.01); G06F 18/22 (2023.01); G06K 9/62 (2022.01); G06V 20/40 (2022.01); G06V 40/16 (2022.01); G06V 40/40 (2022.01); G06V 40/70 (2022.01); G10L 17/22 (2013.01);
U.S. Cl.
CPC ...
G06V 40/40 (2022.01); G06F 18/21 (2023.01); G06F 18/22 (2023.01); G06V 20/49 (2022.01); G06V 40/168 (2022.01); G06V 40/70 (2022.01); G10L 17/22 (2013.01);
Abstract

The embodiments execute machine-learning architectures for biometric-based identity recognition (e.g., speaker recognition, facial recognition) and deepfake detection (e.g., speaker deepfake detection, facial deepfake detection). The machine-learning architecture includes layers defining multiple scoring components, including sub-architectures for speaker deepfake detection, speaker recognition, facial deepfake detection, facial recognition, and lip-sync estimation engine. The machine-learning architecture extracts and analyzes various types of low-level features from both audio data and visual data, combines the various scores, and uses the scores to determine the likelihood that the audiovisual data contains deepfake content and the likelihood that a claimed identity of a person in the video matches to the identity of an expected or enrolled person. This enables the machine-learning architecture to perform identity recognition and verification, and deepfake detection, in an integrated fashion, for both audio data and visual data.


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