公开(公告)号：US20210233541A1

公开(公告)日：2021-07-29

申请号：US17155851

申请日：2021-01-22

Applicant: PINDROP SECURITY, INC.

Inventor： Tianxiang CHEN ,  Elie KHOURY

IPC: G10L17/18 ,  G10L17/04 ,  G10L17/22 ,  G10L17/02

Abstract: Embodiments described herein provide for systems and methods for implementing a neural network architecture for spoof detection in audio signals. The neural network architecture contains a layers defining embedding extractors that extract embeddings from input audio signals. Spoofprint embeddings are generated for particular system enrollees to detect attempts to spoof the enrollee's voice. Optionally, voiceprint embeddings are generated for the system enrollees to recognize the enrollee's voice. The voiceprints are extracted using features related to the enrollee's voice. The spoofprints are extracted using features related to features of how the enrollee speaks and other artifacts. The spoofprints facilitate detection of efforts to fool voice biometrics using synthesized speech (e.g., deepfakes) that spoof and emulate the enrollee's voice.

公开(公告)号：US20250037506A1

公开(公告)日：2025-01-30

申请号：US18918928

申请日：2024-10-17

Applicant: Pindrop Security, Inc.

Inventor： Tianxiang CHEN ,  Elie KHOURY

IPC: G06V40/40 ,  G06F18/21 ,  G06F18/22 ,  G06V20/40 ,  G06V40/16 ,  G06V40/70 ,  G10L17/22

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.

公开(公告)号：US20240363119A1

公开(公告)日：2024-10-31

申请号：US18646375

申请日：2024-04-25

Applicant: Pindrop Security, Inc.

Inventor： Elie KHOURY ,  Ganesh SIVARAMAN ,  Tianxiang CHEN ,  Nikolay GAUBITCH ,  David LOONEY ,  Amit GUPTA ,  Vijay BALASUBRAMANIYAN ,  Nicholas KLEIN ,  Anthony STANKUS

IPC: G10L17/00

CPC classification number: G10L17/00

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. Embodiments include systems and methods for detecting fraudulent presentation attacks using multiple functional engines that implement various fraud-detection techniques, to produce calibrated scores and/or fused scores. A computer may, for example, evaluate the audio quality of speech signals within audio signals, where speech signals contain the speech portions having speaker utterances.

公开(公告)号：US20240363100A1

公开(公告)日：2024-10-31

申请号：US18646228

申请日：2024-04-25

Applicant: Pindrop Security, Inc.

Inventor： Elie KHOURY ,  Ganesh SIVARAMAN ,  Tianxiang CHEN ,  Nikolay GAUBITCH ,  David LOONEY ,  Amit GUPTA ,  Vijay BALASUBRAMANIYAN ,  Nicholas KLEIN ,  Anthony STANKUS

IPC: G10L15/02

CPC classification number: G10L15/02

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. Embodiments include systems and methods for detecting fraudulent presentation attacks using multiple functional engines that implement various fraud-detection techniques, to produce calibrated scores and/or fused scores. A computer may, for example, evaluate the audio quality of speech signals within audio signals, where speech signals contain the speech portions having speaker utterances.

公开(公告)号：US20240153510A1

公开(公告)日：2024-05-09

申请号：US18394300

申请日：2023-12-22

Applicant: PINDROP SECURITY, INC.

Inventor： Tianxiang CHEN ,  Elie KHOURY

IPC: G10L17/18 ,  G10L17/02 ,  G10L17/04 ,  G10L17/08 ,  G10L17/22

CPC classification number: G10L17/18 ,  G10L17/02 ,  G10L17/04 ,  G10L17/08 ,  G10L17/22

Abstract: Embodiments described herein provide for systems and methods for implementing a neural network architecture for spoof detection in audio signals. The neural network architecture contains a layers defining embedding extractors that extract embeddings from input audio signals. Spoofprint embeddings are generated for particular system enrollees to detect attempts to spoof the enrollee's voice. Optionally, voiceprint embeddings are generated for the system enrollees to recognize the enrollee's voice. The voiceprints are extracted using features related to the enrollee's voice. The spoofprints are extracted using features related to features of how the enrollee speaks and other artifacts. The spoofprints facilitate detection of efforts to fool voice biometrics using synthesized speech (e.g., deepfakes) that spoof and emulate the enrollee's voice.

公开(公告)号：US20200322377A1

公开(公告)日：2020-10-08

申请号：US16841473

申请日：2020-04-06

Applicant: PINDROP SECURITY, INC.

Inventor： Khaled LAKHDHAR ,  Parav NAGARSHETH ,  Tianxiang CHEN ,  Elie KHOURY

IPC: H04L29/06 ,  G06N3/08 ,  G06N3/04 ,  G06N5/04 ,  G06N20/10 ,  G06N20/20 ,  G06F17/18 ,  G10L19/26

Abstract: Embodiments described herein provide for automatically detecting whether an audio signal is a spoofed audio signal or a genuine audio signal. A spoof detection system can include an audio signal transforming front end and a classification back end. Both the front end and the back end can include neural networks that can be trained using the same set of labeled audio signals. The audio signal transforming front end can include a one or more neural networks for per-channel energy normalization transformation of the audio signal, and the back end can include a convolution neural network for classification into spoofed or genuine audio signal. In some embodiments, the transforming audio signal front end can include one or more neural networks for bandpass filtering of the audio signals, and the back end can include a residual neural network for audio signal classification into spoofed or genuine audio signal.