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公开(公告)号：US20250037507A1
公开(公告)日：2025-01-30
申请号：US18919049
申请日：2024-10-17
Applicant: Pindrop Security, Inc.
Inventor： Tianxiang CHEN , Elie KHOURY
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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公开(公告)号：US20240363123A1
公开(公告)日：2024-10-31
申请号：US18646310
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
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.
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公开(公告)号：US20240169040A1
公开(公告)日：2024-05-23
申请号：US18515128
申请日：2023-11-20
Applicant: PINDROP SECURITY, INC.
Inventor： Hrishikesh RAO , Ricky CASAL , Elie KHOURY , Eric LORIMER , John CORNWELL , Kailash PATIL
IPC: G06F21/31
CPC classification number: G06F21/316
Abstract: Embodiments include a computing device that executes software routines and/or one or more machine-learning architectures including a neural network-based embedding extraction system that to produce an embedding vector representing a user's behavior's keypresses, where the system extracts the behaviorprint embedding vector using the keypress features that the system references later for authenticating users. Embodiments may extract and evaluate keypress features, such as keypress sequences, keypress pressure or volume, and temporal keypress features, such as the duration of keypresses and the interval between keypresses, among others. Some embodiments employ a deep neural network architecture that generates a behaviorprint embedding vector representation of the keypress duration and interval features that is used for enrollment and at inference time to authenticate users.
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公开(公告)号：US20220301569A1
公开(公告)日：2022-09-22
申请号：US17746832
申请日：2022-05-17
Applicant: Pindrop Security, Inc.
Inventor： Elie KHOURY , Matthew GARLAND
IPC: G10L17/26 , H04L9/40 , G06F21/32 , G06K9/62 , G10L25/30 , G10L17/18 , G10L17/04 , G10L15/26 , G06V40/10 , G06V40/16
Abstract: A score indicating a likelihood that a first subject is the same as a second subject may be calibrated to compensate for aging of the first subject between samples of age-sensitive biometric characteristics. Age of the first subject obtained at a first sample time and age of the second subject obtained at a second sample time may be averaged, and an age approximation may be generated based on at least the age average and an interval between the first and second samples. The age approximation, the interval between the first and second sample times, and an obtained gender of the subject are used to calibrate the likelihood score.
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5.发明申请公开(公告)号：US20210280171A1
公开(公告)日：2021-09-09
申请号：US17192464
申请日：2021-03-04
Applicant: PINDROP SECURITY, INC.
Inventor： Kedar PHATAK , Elie KHOURY
Abstract: Embodiments described herein provide for audio processing operations that evaluate characteristics of audio signals that are independent of the speaker's voice. A neural network architecture trains and applies discriminatory neural networks tasked with modeling and classifying speaker-independent characteristics. The task-specific models generate or extract feature vectors from input audio data based on the trained embedding extraction models. The embeddings from the task-specific models are concatenated to form a deep-phoneprint vector for the input audio signal. The DP vector is a low dimensional representation of the each of the speaker-independent characteristics of the audio signal and applied in various downstream operations.
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Patent Agency Ranking
公开(公告)号：US20210110813A1
公开(公告)日：2021-04-15
申请号：US17066210
申请日：2020-10-08
Applicant: PINDROP SECURITY, INC.
Inventor： Elie KHOURY , Ganesh SIVARAMAN , Tianxiang CHEN , Amruta VIDWANS
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.
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公开(公告)号：US20200294510A1
公开(公告)日：2020-09-17
申请号：US16889337
申请日：2020-06-01
Applicant: PINDROP SECURITY, INC.
Inventor： Elie KHOURY , Matthew GARLAND
IPC: G10L17/26 , G10L15/26 , H04L29/06 , G06K9/00 , G06F21/32 , G06K9/62 , G10L25/30 , G10L17/18 , G10L17/04
Abstract: A score indicating a likelihood that a first subject is the same as a second subject may be calibrated to compensate for aging of the first subject between samples of age-sensitive biometric characteristics. Age of the first subject obtained at a first sample time and age of the second subject obtained at a second sample time may be averaged, and an age approximation may be generated based on at least the age average and an interval between the first and second samples. The age approximation, the interval between the first and second sample times, and an obtained gender of the subject are used to calibrate the likelihood score.
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公开(公告)号：US20190392842A1
公开(公告)日：2019-12-26
申请号：US16536293
申请日：2019-08-08
Applicant: PINDROP SECURITY, INC.
Inventor： Elie KHOURY , Matthew GARLAND
Abstract: The present invention is directed to a deep neural network (DNN) having a triplet network architecture, which is suitable to perform speaker recognition. In particular, the DNN includes three feed-forward neural networks, which are trained according to a batch process utilizing a cohort set of negative training samples. After each batch of training samples is processed, the DNN may be trained according to a loss function, e.g., utilizing a cosine measure of similarity between respective samples, along with positive and negative margins, to provide a robust representation of voiceprints.
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公开(公告)号：US20240363125A1
公开(公告)日：2024-10-31
申请号：US18646493
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
CPC classification number: G10L17/26 , G10L17/02 , G10L17/04 , G10L25/60 , H04M3/2281 , H04M3/5183 , H04M2201/405
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.
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公开(公告)号：US20240355334A1
公开(公告)日：2024-10-24
申请号：US18388457
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: G10L17/06
CPC classification number: G10L17/06
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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