Alibaba Group Holding Limited

Advanced New Technologies Co., Ltd.

Zhejiang University

Hangzhou Ags Medtech Co., Ltd.

Alipay Information Technology Co., Ltd.

Peng Li

Jidong Chen

Mian Huang

Chaochao Chen

Dandan Zheng

Wei Xu

Wenbo Yang

Le He

Zhaoyu Chen

Jun Zhou

Jing Li

Baiming Shi

Jingting Ding

Jiajiong Cao

Yongling Xue

Junfei Liu

Yukun Tuo

Yongxiang Xie

Luzhe Huang

Yiming Fu

Yipeng Sun

A three-dimensional (3D) optical coherence tomography angiography (OCTA) method and system based on feature space is provided. OCT signals of scattering samples in a 3D space are acquired by a collector; a theoretical classifier, local signal-noise ratios (SNRs) and decorrelation coefficients of the OCT scattering signals are combined to establish a two-dimensional (2D) feature space to realize classification of dynamic blood flow signals and static tissues. Specifically, computational analysis of first-order and zero-order autocovariance is used to obtain two features of SNR and decorrelation of each OCT scattering signal; a 2D inverse SNR (iSNR)-decorrelation feature (ID) space is established; and based on the multivariate time series theory, a linear classifier is established in the ID space to remove a static surrounding tissue background. The 3D OCTA method and system can improve the contrast of blood flow images and-improve the accuracy of blood flow quantification.

Three-dimensional (3D) optical coherence tomography angiography (OCTA) method and system based on feature space

Disclosed herein are methods, systems, and apparatus, including computer programs encoded on computer storage media for secure collaborative computation of a matrix product of a first matrix including private data of a first party and a second matrix including private data of the second party by secret sharing without a trusted initializer. One method includes obtaining a first matrix including private data of the first party; generating a first random matrix; identifying a first sub-matrix and a second sub-matrix of the first random matrix; computing first scrambled private data of the first party based on the first matrix, the first random matrix, the first sub-matrix, and the second sub-matrix; receiving second scrambled private data of the second party; computing a first addend of the matrix product; receiving a second addend of the matrix product; and computing the matrix product by summing the first addend and the second addend.

Secure multi-party computation with no trusted initializer

A first training participant performs an iterative process until a predetermined condition is satisfied, where the iterative process includes: obtaining, using secret sharing matrix addition and based on the current sub-model of each training participant and a corresponding feature sample subset of each training participant, a current prediction value of the regression model for a feature sample set, where the corresponding feature sample subset of each training participant is obtained by performing vertical segmentation on the feature sample set; determining a prediction difference between the current prediction value and a label corresponding to the current prediction value; sending the prediction difference to each second training participant; and updating a current sub-model of the first training participant based on the current sub-model of the first training participant and a product of a corresponding feature sample subset of the first training participant and the prediction difference.

Model training methods, apparatuses, and systems

Embodiments of the present application relate to a method, device, and system for network control. The method includes determining, by one or more processors, whether a target application is running in a foreground of a terminal, the target application being included in a set of target applications, and in response to determining that the target application is running in the foreground, blocking or throttling, by the one or more processors, network communication for at least one application not included in the set of target applications.

Method, system, and device for network control

This specification discloses a user identity verification method, apparatus, and system, relating to the field of information technology. The method comprises: receiving a facial image and one or more eye-print pair images corresponding to an identity verification object from a client, wherein a number of the one or more eye-print pair images corresponds to a number of eye-print collection steps, comparing the facial image to a preset facial image and comparing the one or more eye-print pair images to preset eye-print templates, and sending successful identity verification information to a client when comparison results for the facial image and the one or more eye-print pair images meet preset conditions.

User identity verification method, apparatus and system

An identity authentication procedure of a user is initiated and a plurality of virtual reality articles is displayed. Selection operation information of the user is determined for the plurality of virtual reality articles. Whether the selection operation information matches predefined standard selection operation information is determined. In response to determining that the selection operation information matches the predefined standard selection operation information, whether the identity authentication procedure of the user succeeds is determined.

Methods and apparatuses for identity authentication in virtual reality

An audio/video stream generated by a target object to be authenticated is obtained. The target object is associated with a user. A determination is made whether a lip reading component and voice component in the audio/video stream are consistent. In response to determining that the lip reading component and voice component are consistent, voice recognition is performed on an audio stream in the audio/video stream to obtain voice content. The voice content is used as an object identifier of the target object. A model physiological feature corresponding to the object identifier is obtained from object registration information. Physiological recognition is performed on the audio/video stream to obtain a physiological feature of the target object. The physiological feature of the target object is compared with the model physiological feature to obtain a comparison result. If the comparison result satisfies an authentication condition, the target object is authenticated.

Identity authentication method and apparatus

Disclosed in the application are document verification and identity verification methods and devices. A computer-implemented document verification method comprises: obtaining an image of a to-be-verified document; and verifying whether an anti-counterfeiting identifier exists in the image; verifying a source of the image; and obtaining a verification result for the to-be-verified document based on the verification of the anti-counterfeiting identifier and the verification of the source of the image. A computer-implemented identity verification method comprises: obtaining an image of an identity document of a to-be-verified user; obtaining a first verification result for the identity document based on the image of the identity document; verifying authenticity of identity information in the image of the identify document to obtain a second verification result; and determining an identity verification result for the to-be-verified user based on the first verification result and the second verification result.

Document verification system, device, and method using a classification model

This specification describes techniques for verifying authenticity of an image. One example method includes identifying a baseline image depicting a baseline background; identifying a comparison image depicting a card; identifying a comparison background in the comparison image, wherein the comparison background is an area of the comparison image other than an area occupied by the card; determining a probability that the baseline background matches the comparison background; determining that the probability satisfies a verification threshold; and in response to determining that the probability satisfies a verification threshold, determining that the comparison image was acquired by capturing an image of a physical card corresponding to the card depicted in the comparison image.

Method and apparatus for verifying certificates and identities

Liveness detection and an identity authentication is included in the disclosure. A user's biological characteristic information is collected and displayed at an initial position on a screen of a computing device. A target position is determined using the initial position, and the target position is displayed on the screen. The user is prompted to move the user's biological characteristic information to cause the displayed biological characteristic to move from the initial position on the screen to the target position on the screen. The user's movement is detected and the display position of the displayed biological characteristic information is determined using the detected user's movement; a judgment is made whether the user is a living being using a relationship between the determined display position and the target position. The biological characteristic information of a living being can be verified, e.g., when the user logs in, thereby improving security.

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Hangzhou, China

Peng Li