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    ROBUST SPREAD-SPECTRUM SPEECH WATERMARKING USING LINEAR PREDICTION AND DEEP SPECTRAL SHAPING
    62.
    发明申请
    ROBUST SPREAD-SPECTRUM SPEECH WATERMARKING USING LINEAR PREDICTION AND DEEP SPECTRAL SHAPING 有权

    公开(公告)号：US20250095662A1

    公开(公告)日：2025-03-20

    申请号：US18883681

    申请日：2024-09-12

    Applicant: Pindrop Security, Inc.

    Inventor： David Looney ,  Nikolay Gaubitch

    IPC: G10L19/018 ,  G10L25/30

    Abstract: Embodiments disclosed herein include software processes executed by a computer for encoding and decoding watermarks for a speech signal in a call signal communicated via telephony channels. An encoder uses Linear Predictive Coding (LPC) to analyzes the call signal's spectral envelope and embeds the watermark into the LPC log-spectrum of the speech signal of the call signal. The encoder may reduce the watermark's strength at a formant peak of the speech signal, balancing the watermark's robustness and detectability. A deep decoder includes a neural network architecture trained on watermarked and watermark-free speech signals having various types of degradation to extract a feature vector of a call signal and compute a watermark detection score for one or more frames or for the call signal. At inference time, the deep decoder detects the watermark when the watermark detection score satisfies a detection threshold.

    METHOD AND APPARATUS FOR THREAT IDENTIFICATION THROUGH ANALYSIS OF COMMUNICATIONS SIGNALING, EVENTS, AND PARTICIPANTS
    63.
    发明申请
    METHOD AND APPARATUS FOR THREAT IDENTIFICATION THROUGH ANALYSIS OF COMMUNICATIONS SIGNALING, EVENTS, AND PARTICIPANTS 有权

    公开(公告)号：US20250071200A1

    公开(公告)日：2025-02-27

    申请号：US18943686

    申请日：2024-11-11

    Applicant: Pindrop Security, Inc.

    Inventor： Lance Douglas

    IPC: H04M3/22 ,  H04L9/40 ,  H04M3/436 ,  H04M7/00

    Abstract: Aspects of the invention determining a threat score of a call traversing a telecommunications network by leveraging the signaling used to originate, propagate and terminate the call. Outer-edge data utilized to originate the call may be analyzed against historical, or third party real-time data to determine the propensity of calls originating from those facilities to be categorized as a threat. Storing the outer edge data before the call is sent over the communications network permits such data to be preserved and not subjected to manipulations during traversal of the communications network. This allows identification of threat attempts based on the outer edge data from origination facilities, thereby allowing isolation of a compromised network facility that may or may not be known to be compromised by its respective network owner. Other aspects utilize inner edge data from an intermediate node of the communications network which may be analyzed against other inner edge data from other intermediate nodes and/or outer edge data.

    Method and apparatus for threat identification through analysis of communications signaling, events, and participants
    64.
    发明授权
    Method and apparatus for threat identification through analysis of communications signaling, events, and participants 有权

    公开(公告)号：US12143531B2

    公开(公告)日：2024-11-12

    申请号：US17943893

    申请日：2022-09-13

    Applicant: Pindrop Security, Inc.

    Inventor： Lance Douglas

    IPC: H04M3/22 ,  H04L9/40 ,  H04M3/436 ,  H04M7/00

    Abstract: Aspects of the invention determining a threat score of a call traversing a telecommunications network by leveraging the signaling used to originate, propagate and terminate the call. Outer-edge data utilized to originate the call may be analyzed against historical, or third party real-time data to determine the propensity of calls originating from those facilities to be categorized as a threat. Storing the outer edge data before the call is sent over the communications network permits such data to be preserved and not subjected to manipulations during traversal of the communications network. This allows identification of threat attempts based on the outer edge data from origination facilities, thereby allowing isolation of a compromised network facility that may or may not be known to be compromised by its respective network owner. Other aspects utilize inner edge data from an intermediate node of the communications network which may be analyzed against other inner edge data from other intermediate nodes and/or outer edge data.

    Audiovisual deepfake detection
    65.
    发明授权
    Audiovisual deepfake detection 有权

    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.

    DEEPFAKE DETECTION
    67.
    发明公开
    DEEPFAKE DETECTION 审中-公开

    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.

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