视频实时AWS实时人工检测 [英] AWS live human detection from video

问题描述

是否有任何AWS api可用于检测视频中的直播人?例如，一个人可以通过仅显示另一个人的图像来伪造人类检测.那么有没有办法克服这个问题呢? 识别将首先验证视频中是否有人. 如果不是，AWS是否还有其他API或python库可以做到这一点?

Is there any AWS api for detecting live human in a video ? For example a person can fake the human detection by just showing an image of another person. So is there a way to overcome this ? The recognition will first verify if there is a person in the video or not. If not AWS is there any other api or python libraries to do that?

推荐答案

根据预期的攻击媒介，您有责任制定一个由其他身份验证构件组成的解决方案. Amazon Rekognition提供了可用于这些任务的功能.

Depending on the expected attack vector(s), it is likely going to be your responsibility to craft a solution comprised of other identity verification building blocks. Amazon Rekognition offers functionality that can be used for these tasks.

根据您的用户群的预期危害程度，一个样本数据点(图像)可能不足以可靠地确定受试者是否是人类(尽管是特定的).如果没有其他数据点(例如深度传感器，热成像等)，则很难确定性地确定某人是否试图对模仿的面孔进行模糊处理.

Depending on the expected levels of nefariousness of your userbase, one single sample data point (image) may not be sufficient for being able to robustly determine whether the subject is a human or not (nonetheless a specific one). Without additional datapoints like depth sensors, thermal imaging, and more, it is hard to definitively determine if someone is attempting to obfuscate with a mimicked face.

提高这种系统的鲁棒性的一种方法是为用户设计具有自定义半随机测试"的多因素身份验证层，其中包括不会被物理攻击媒介欺骗的其他信息测试.增加光学系统的鲁棒性的另一种方法是在录制视频的同时要求人们采取一系列姿势或任务(掩盖嘴巴，移交右眼，吐出舌头)，这些姿势或任务对真实人来说很容易做到，这样的模仿.

One method for increasing the robustness of such a system is to craft a multi-factor authentication layer with custom semi-random "tests" for users, including other information tests that would not be spoofed by physical attack vectors. A further way to increase robustness of the optical system would be to record video while asking the person to assume a sequence of poses or tasks (cover mouth, hand over right eye, tongue out) that are easy for a real person to do but not an imitation like this.

Amazon Rekognition 支持识别视频尤其可用于检测用户从一个视频更改时的更改.摆姿势到下一个试图自动发现坏演员.

Amazon Rekognition supports finding faces within an image, as well as matching a test face to faces in a collection, and can also be used to help estimate other meta-concepts like emotion (happy, sad, frown, smile, etc). Rekognition video in particular can be used to detect changes while the user is changing from one pose to the next in an attempt to auto-detect bad actors.

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