公开(公告)号：US12142083B2

公开(公告)日：2024-11-12

申请号：US17503152

申请日：2021-10-15

Applicant: Pindrop Security, Inc.

Inventor： Tianxiang Chen ,  Elie Khoury

IPC: G06K9/00 ,  G06F18/21 ,  G06F18/22 ,  G06K9/62 ,  G06V20/40 ,  G06V40/16 ,  G06V40/40 ,  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：US20240355337A1

公开(公告)日：2024-10-24

申请号：US18388364

申请日：2023-11-09

Applicant: PINDROP SECURITY, INC.

Inventor： Umair Altaf ,  Sai Pradeep Peri ,  Lakshay Phatela ,  Payas Gupta ,  Yitao Sun ,  Svetlana Afanaseva ,  Kailash Patil ,  Elie Khoury ,  Bradley Magnetta ,  Vijay Balasubramaniyan ,  Tianxiang Chen

IPC: G10L17/24

CPC classification number: G10L17/24

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice “liveness” detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.

公开(公告)号：US20240363099A1

公开(公告)日：2024-10-31

申请号：US18388466

申请日：2023-11-09

Applicant: PINDROP SECURITY, INC.

Inventor： Umair Altaf ,  Sai Pradeep Peri ,  Lakshay Phatela ,  Payas Gupta ,  Yitao Sun ,  Svetlana Afanaseva ,  Kailash Patil ,  Elie Khoury ,  Bradley Magnetta ,  Vijay Balasubramaniyan ,  Tianxiang Chen

IPC: G10L15/02 ,  G10L15/16

CPC classification number: G10L15/02 ,  G10L15/16

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice “liveness” detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.

公开(公告)号：US20240355319A1

公开(公告)日：2024-10-24

申请号：US18388385

申请日：2023-11-09

Applicant: PINDROP SECURITY, INC.

Inventor： Umair Altaf ,  Sai Pradeep Peri ,  Lakshay Phatela ,  Payas Gupta ,  Yitao Sun ,  Svetlana Afanaseva ,  Kailash Patil ,  Elie Khoury ,  Bradley Magnetta ,  Vijay Balasubramaniyan ,  Tianxiang Chen

IPC: G10L15/02 ,  G10L15/16

CPC classification number: G10L15/02 ,  G10L15/16

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice “liveness” detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.

公开(公告)号：US12015637B2

公开(公告)日：2024-06-18

申请号：US16841473

申请日：2020-04-06

Applicant: PINDROP SECURITY, INC.

Inventor： Khaled Lakhdhar ,  Parav Nagarsheth ,  Tianxiang Chen ,  Elie Khoury

IPC: H04L9/40 ,  G06F17/18 ,  G06N3/045 ,  G06N3/084 ,  G06N20/10 ,  G10L17/00 ,  G10L17/04 ,  G10L17/26 ,  G10L19/26 ,  H04L65/75

CPC classification number: H04L63/1466 ,  G06F17/18 ,  G06N3/045 ,  G06N3/084 ,  G06N20/10 ,  G10L17/00 ,  G10L17/04 ,  G10L17/26 ,  G10L19/26 ,  H04L65/75

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.

公开(公告)号：US11862177B2

公开(公告)日：2024-01-02

申请号：US17155851

申请日：2021-01-22

Applicant: PINDROP SECURITY, INC.

Inventor： Tianxiang Chen ,  Elie Khoury

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

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：US20240363103A1

公开(公告)日：2024-10-31

申请号：US18388412

申请日：2023-11-09

Applicant: Pindrop Security, Inc.

Inventor： Umair Altaf ,  Sai Pradeep Peri ,  Lakshay Phatela ,  Payas Gupta ,  Yitao Sun ,  Svetlana Afanaseva ,  Kailash Patil ,  Elie Khoury ,  Bradley Magnetta ,  Vijay Balasubramaniyan ,  Tianxiang Chen

IPC: G10L15/08

CPC classification number: G10L15/08

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice “liveness” detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.

公开(公告)号：US20240355322A1

公开(公告)日：2024-10-24

申请号：US18388428

申请日：2023-11-09

Applicant: Pindrop Security, Inc.

Inventor： Umair Altaf ,  Sai Pradeep Peri ,  Lakshay Phatela ,  Payas Gupta ,  Yitao Sun ,  Svetlana Afanaseva ,  Kailash Patil ,  Elie Khoury ,  Bradley Magnetta ,  Vijay Balasubramaniyan ,  Tianxiang Chen

IPC: G10L15/08

CPC classification number: G10L15/08 ,  G06N20/00

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice “liveness” detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.

公开(公告)号：US11715460B2

公开(公告)日：2023-08-01

申请号：US17066210

申请日：2020-10-08

Applicant: PINDROP SECURITY, INC.

Inventor： Elie Khoury ,  Ganesh Sivaraman ,  Tianxiang Chen ,  Amruta Vidwans

IPC: G10L15/16 ,  G10L25/51 ,  G10L17/04 ,  G10L15/06

CPC classification number: G10L15/16 ,  G10L15/063 ,  G10L17/04 ,  G10L25/51

Abstract: Described herein are systems and methods for improved audio analysis using a computer-executed neural network having one or more in-network data augmentation layers. The systems described herein help ease or avoid unwanted strain on computing resources by employing the data augmentation techniques within the layers of the neural network. The in-network data augmentation layers will produce various types of simulated audio data when the computer applies the neural network on an inputted audio signal during a training phase, enrollment phase, and/or testing phase. Subsequent layers of the neural network (e.g., convolutional layer, pooling layer, data augmentation layer) ingest the simulated audio data and the inputted audio signal and perform various operations.

公开(公告)号：US20230005486A1

公开(公告)日：2023-01-05

申请号：US17855149

申请日：2022-06-30

Applicant: Pindrop Security, Inc.

Inventor： Tianxiang Chen ,  Elie Khoury

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

Abstract: Embodiments include a computer executing voice biometric machine-learning for speaker recognition. The machine-learning architecture includes embedding extractors that extract embeddings for enrollment or for verifying inbound speakers, and embedding convertors that convert enrollment voiceprints from a first type of embedding to a second type of embedding. The embedding convertor maps the feature vector space of the first type of embedding to the feature vector space of the second type of embedding. The embedding convertor takes as input enrollment embeddings of the first type of embedding and generates as output converted enrolled embeddings that are aggregated into a converted enrolled voiceprint of the second type of embedding. To verify an inbound speaker, a second embedding extractor generates an inbound voiceprint of the second type of embedding, and scoring layers determine a similarity between the inbound voiceprint and the converted enrolled voiceprint, both of which are the second type of embedding.