基于GRU的智能手机多场景步态认证

姜奇; 冯茹; 张瑞杰; 王金花; 陈婷; 魏福山

doi:10.11959/j.issn.2096-109x.2022060

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基于GRU的智能手机多场景步态认证

专题：大数据与人工智能安全 | 更新时间：2024-06-03

- 基于GRU的智能手机多场景步态认证
- GRU-based multi-scenario gait authentication for smartphones
- 网络与信息安全学报 2022年8卷第5期页码：26-39
- 作者机构：
  
  1. 西安电子科技大学网络与信息安全学院, 陕西西安 710126
  2. 河南省网络密码技术重点实验室，河南郑州 450001
  3. 信息工程大学密码工程学院，河南郑州 450001
  4. 保密通信重点实验室，四川成都 610041
  5. 信息工程大学网络空间安全学院，河南郑州 450001
- 作者简介：
  
  [ "姜奇（1983- ），男，安徽全椒人，西安电子科技大学教授、博士生导师，主要研究方向为密码协议，物联网安全" ]
  [ "冯茹（1997- ），女，山西晋城人，西安电子科技大学硕士生，主要研究方向为生物认证" ]
  [ "张瑞杰（1984- ），女，河南郑州人，信息工程大学讲师，主要研究方向为人工智能、网络信息防御等" ]
  [ "王金花（1995- ），女，山西太原人，保密通信重点实验室助理工程师，主要研究方向为密码协议设计与分析" ]
  [ "陈婷（1995- ），女，江西赣州人，保密通信重点实验室助理工程师，主要研究方向为网络安全、密码协议设计与分析" ]
  [ "魏福山（1983- ），男，甘肃武威人，信息工程大学副教授、博士生导师，主要研究方向为安全协议设计与分析" ]
- 基金信息：
  
  国家自然科学基金;The National Natural Science Foundation of China(92167203);国家自然科学基金;The National Natural Science Foundation of China(62072352);国家自然科学基金;The National Natural Science Foundation of China(62125205);陕西省教育厅科研计划项目;Scientific Research Program Funded by Education Department of Shaanxi Provincial Government(20JY016);陕西省重点产业链项目;Key Industrial Chain Projects in Shaanxi Province(2020ZDLGY09-06)
- DOI：10.11959/j.issn.2096-109x.2022060
  中图分类号： TP393
- 网络首发：2022-10，
  
  纸质出版：2022-10-15
- 稿件说明：
移动端阅览
姜奇, 冯茹, 张瑞杰, 等. 基于GRU的智能手机多场景步态认证[J]. 网络与信息安全学报, 2022,8(5):26-39.

Qi JIANG, Ru FENG, Ruijie ZHANG, et al. GRU-based multi-scenario gait authentication for smartphones[J]. Chinese Journal of Network and Information Security, 2022, 8(5): 26-39.
姜奇, 冯茹, 张瑞杰, 等. 基于GRU的智能手机多场景步态认证[J]. 网络与信息安全学报, 2022,8(5):26-39. DOI： 10.11959/j.issn.2096-109x.2022060.

Qi JIANG, Ru FENG, Ruijie ZHANG, et al. GRU-based multi-scenario gait authentication for smartphones[J]. Chinese Journal of Network and Information Security, 2022, 8(5): 26-39. DOI： 10.11959/j.issn.2096-109x.2022060.

摘要

基于步态的智能手机认证研究大多针对单一受控场景，而未考虑多场景变化对认证准确性的影响。智能手机以及用户的运动方向在不同场景下会发生变化，当使用对方向变化敏感的传感器采集用户步态数据时，可能会因场景的改变出现一定的偏差。因此，为智能手机提供一种多场景下的高精度步态认证方法已成为亟待解决的问题。此外，模型训练算法的选取是决定步态认证准确率和效率的关键。目前流行的基于长短期记忆（LSTM，long short-term memory）网络的认证模型能够实现较高的认证准确率，但其训练参数较多，内存占用较大，训练效率有待提升。针对现有步态认证方案未满足多场景认证需求、模型认证和训练且难以兼顾高效率与高准确率的问题，提出了基于门控循环单元（GRU，gate recurrent unit）的智能手机多场景步态认证方案。通过小波变换对步态信号进行初步降噪处理，并采用自适应的步态周期分割算法对循环的步态信号进行切分。为满足多步态场景的认证需求，采用坐标系转换方法对步态信号进行方向无关性处理，以消除智能手机方向以及用户运动方向对认证结果的影响。为实现高准确率认证以及高效率训练模型，利用不同体系结构的 GRU 以及多种优化方式训练用户步态模型。在公开数据集 PSR 和ZJU-GaitAcc上对所提方案进行实验分析。与所列方案对比，所提方案提高了认证准确率，较之基于LSTM的步态认证模型，所提模型的训练效率提升了约20%。

Abstract

At present

most of the gait-based smartphone authentication researches focus on a single controlled scenario without considering the impact of multi-scenario changes on the authentication accuracy.The movement direction of the smartphone and the user changes in different scenarios

and the user’s gait data collected by the orientation-sensitive sensor will be biased accordingly.Therefore

it has become an urgent problem to provide a multi-scenario high-accuracy gait authentication method for smartphones.In addition

the selection of the model training algorithm determines the accuracy and efficiency of gait authentication.The current popular authentication model based on long short-term memory (LSTM) network can achieve high authentication accuracy

but it has many training parameters

large memory footprint

and the training efficiency needs to be improved.In order to solve the above problems a multi-scenario gait authentication scheme for smartphones based on Gate Recurrent Unit (GRU) was proposed.The gait signals were preliminarily denoised by wavelet transform

and the looped gait signals were segmented by an adaptive gait cycle segmentation algorithm.In order to meet the authentication requirements of multi-scenario

the coordinate system transformation method was used to perform direction-independent processing on the gait signals

so as to eliminate the influence of the orientation of the smartphone and the movement of the user on the authentication result.Besides

in order to achieve high-accuracy authentication and efficient model training

GRUs with different architectures and various optimization methods were used to train the gait model.The proposed scheme was experimentally analyzed on publicly available datasets PSR and ZJU-GaitAcc.Compared with the related schemes

the proposed scheme improves the authentication accuracy.Compared with the LSTM-based gait authentication model

the training efficiency of the proposed model is improved by about 20%.

关键词

Keywords

references

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