公开(公告)号：US12300035B1

公开(公告)日：2025-05-13

申请号：US17708492

申请日：2022-03-30

Applicant: Amazon Technologies, Inc.

Inventor： Xiang Xu ,  Mingze Xu ,  Zheng Zhang ,  Yuanjun Xiong ,  Wei Xia ,  Jonathan Wu ,  Joseph P Tighe

IPC: G06V40/40 ,  G06T7/00 ,  G06T7/20 ,  G06T7/70 ,  G06T11/00 ,  G06V10/764 ,  G06V10/82 ,  G06V20/40 ,  G06V40/16

Abstract: Techniques for liveness detection using a motion, face, and context cues. The techniques can be implemented to prevent against successful presentation attacks, video injection attacks, and deepfake attacks. In some examples, the techniques encompass receiving a set of video frames from a personal computing device. A first liveness determination can be made using a motion-based model based on the received video frames. A second liveness determination can be made using a face-based model based on the received video frames. A third liveness determination can be made using a context-based model based on the received video frames. A final liveness determination can be made based on the first, second and third liveness determinations.

公开(公告)号：US11860977B1

公开(公告)日：2024-01-02

申请号：US17307701

申请日：2021-05-04

Applicant: Amazon Technologies, Inc.

Inventor： Yifan Xing ,  Tianjun Xiao ,  Tong He ,  Yongxin Wang ,  Yuanjun Xiong ,  Wei Xia ,  David Paul Wipf ,  Zheng Zhang ,  Stefano Soatto

IPC: G06F18/2323 ,  G06N20/00 ,  G06F18/2415 ,  G06F18/23213 ,  G06F18/2413

CPC classification number: G06F18/2323 ,  G06F18/23213 ,  G06F18/2415 ,  G06F18/24147 ,  G06N20/00

Abstract: Techniques for performing visual clustering with a hierarchical graph neural network framework including a joint linkage prediction and density estimation graph model are described. Embodiments herein recurrently run the joint linkage prediction and density estimation graph model to generate intermediate clusters in multiple iterations (e.g., until convergence) to obtain a final clustering result. In certain embodiments, for each iteration, the input graph contains nodes that are merged from nodes assigned to intermediate clusters from the previous iteration. By using a small and fixed bandwidth k in each iteration, embodiments herein alleviate the sensitivity to the k selection for different clustering applications. Certain embodiments herein remove the tuning of a different k (e.g., k-bandwidth) for k-nearest neighbor graph construction over different clustering applications.

公开(公告)号：US12118324B1

公开(公告)日：2024-10-15

申请号：US17710727

申请日：2022-03-31

Applicant: Amazon Technologies, Inc.

Inventor： Li Zhang ,  Sanjiv Ranjan Das ,  Yue Zhao ,  Zhijiang He ,  Shenghua Yue ,  Zheng Zhang ,  Xin Huang ,  Sheng Zha ,  Shuai Zheng

IPC: G06F16/35 ,  G06F16/34 ,  G06F40/284 ,  G06F40/40

CPC classification number: G06F40/40 ,  G06F16/345 ,  G06F16/355 ,  G06F40/284

Abstract: Techniques for machine learning (ML) and natural language processing (NLP) are described. One technique enables the creation of a clean training dataset through just a few API calls. Another technique provides an automated process for generating a domain-specific lexicon, which is then used to generate ML training datasets, in a manner that requires little to no human labor. Another technique gathers ML training data from domain-specific public sources, which are more likely than typical public sources to contain focused terminology and to be free from errors, thus resulting in trained ML models that provide more accurate inferences.