基于可视数据的可信身份识别和认证方法

彭春蕾; 高新波; 王楠楠; 李洁

doi:10.11959/j.issn.1000-0801.2020293

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基于可视数据的可信身份识别和认证方法

综述 | 更新时间：2024-06-05

- 基于可视数据的可信身份识别和认证方法
- Dependable identity recognition and authorization based on visual information
- 电信科学 2020年36卷第11期页码：1-17
- 作者机构：
  
  1. 西安电子科技大学综合业务网理论及关键技术国家重点实验室，陕西西安 710071
  2. 重庆邮电大学重庆市图像认知重点实验室，重庆 400065
- 作者简介：
  
  [ "彭春蕾（1991- ），男，博士，西安电子科技大学综合业务网理论及关键技术国家重点实验室讲师，主要研究方向为计算机视觉、模式识别和机器学习" ]
  [ "高新波（1972- ），男，博士，重庆邮电大学重庆市图像认知重点实验室教授，主要研究方向为多媒体分析、计算机视觉、模式识别与机器学习等" ]
  [ "王楠楠（1986- ），男，博士，西安电子科技大学综合业务网理论及关键技术国家重点实验室教授，主要研究方向为计算机视觉、模式识别和机器学习" ]
  [ "李洁（1972- ），女，博士，西安电子科技大学综合业务网理论及关键技术国家重点实验室教授，主要研究方向为模式识别和计算机视觉" ]
- 基金信息：
  
  国家重点研发计划基金资助项目;The National Key Research and Development Program of China(2016QY01W0200);国家重点研发计划基金资助项目;The National Key Research and Development Program of China(2018AAA0103202);国家自然科学基金资助项目;The National Natural Science Foundation of China(61806152);陕西省重点研发项目;The Key Research and Development Program of Shaanxi(2020ZDLGY08-08);陕西省自然科学基金资助项目;The Natural Science Basic Research Plan in Shaanxi Province of China(2019JM-289)
- DOI：10.11959/j.issn.1000-0801.2020293
  中图分类号： TP393
- 网络出版日期：2020-11，
  
  纸质出版日期：2020-11-20
- 稿件说明：
移动端阅览
彭春蕾, 高新波, 王楠楠, 等. 基于可视数据的可信身份识别和认证方法[J]. 电信科学, 2020,36(11):1-17.

Chunlei PENG, Xinbo GAO, Nannan WANG, et al. Dependable identity recognition and authorization based on visual information[J]. Telecommunications science, 2020, 36(11): 1-17.
彭春蕾, 高新波, 王楠楠, 等. 基于可视数据的可信身份识别和认证方法[J]. 电信科学, 2020,36(11):1-17. DOI： 10.11959/j.issn.1000-0801.2020293.

Chunlei PENG, Xinbo GAO, Nannan WANG, et al. Dependable identity recognition and authorization based on visual information[J]. Telecommunications science, 2020, 36(11): 1-17. DOI： 10.11959/j.issn.1000-0801.2020293.

摘要

近年来，深度学习技术在基于视频和图像等可视数据的身份识别和认证任务（如人脸、行人识别等）中得到了广泛应用。然而，机器学习（特别是深度学习模型）容易受到特定的对抗攻击干扰，从而误导身份识别系统做出错误的判断。因此，针对身份识别系统的可信认证技术研究逐渐成为当前的研究热点。分别从基于信息空间和物理空间的可视数据身份识别和认证攻击方法展开介绍，分析了针对人脸检测与识别系统、行人重识别系统的攻击技术及进展，以及基于人脸活体伪造和可打印对抗图案的物理空间攻击方法，进而讨论了可视数据身份匿名化和隐私保护技术。最后，在简要介绍现有研究中采用的数据库、实验设置与性能分析的基础上，探讨了可能的未来研究方向。

Abstract

Recently

deep learning has been widely applied to video and image based identity recognition and authorization tasks

including face recognition and person identification.However

machine learning models

especially deep learning models

can be easily fooled by adversarial attacks

which may cause the identity recognition systems to make a wrong decision.Therefore

dependable identity recognition and authorization has become one of the hot topics currently.Recent advances on dependable identity recognition and authorization from both information space and physical space were presented

where the development of the attack models on face detection

face recognition

person re-identification

and face anti-spoofing as well as printable adversarial patches were introduced.The algorithms of visual identity anonymization and privacy protection were further discussed.Finally

the datasets

experimental protocols and performance of dependable identity recognition methods were summarized

and the possible directions in the future research were presented.

关键词

Keywords

references

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