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摘要:
耳戴式设备作为典型智能物联网边端感知设备应用场景众多，保护其合法用户隐私以及防止非法使用至关重要. 针对当前耳戴式设备用户身份认证方法受输入界面、传感器成本以及设备功耗等限制导致安全性不足、普适性不高以及用户体验不佳等问题，提出一种基于耳戴式设备内置惯性测量单元的用户身份认证方法，该方法通过采集用户执行面部交互手势所产生的振动信号来提取用户特异性信息，并基于上述信息的智能分析实现多样化的隐式用户连续身份认证. 为了提取精准可靠的用户特异性信息，提出了一种基于孪生网络的深度神经网络特征编码器，将同一用户的手势样本映射到特征空间中更近的位置，放大不同用户的手势样本之间的距离，实现用户特异性信息的有效编码. 对于基于用户特异性信息的用户身份连续认证，提出了一种基于单类支持向量机超平面距离的加权投票策略，能够自适应地优化判别边界来更好地捕捉蕴含的特征和结构，根据超平面内外样本点与超平面的距离决定该样本的置信程度，以此设计加权投票实现认证. 实验结果表明，所提方法在单次投票中实现了97.33%的认证准确率，7轮投票的连续认证后取得99.993%的认证准确率，优于对比的所有方法，无需密码的同时提供更流畅的用户体验和更高级别的安全性，具有较高的实际应用价值.
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关键词:
- 智能物联网 /
- 耳戴式设备 /
- 用户连续认证 /
- 深度神经网络特征编码器 /
- 加权投票策略
Abstract:Earable devices are used as typical AIoT edge sensing devices. Protecting the privacy of legitimate users and preventing illegal use has become extremely important. In response to the current user authentication methods for earable devices, which are limited by input interfaces, sensor costs, and device power consumption, resulting in insufficient security, low universality, and poor user experience, a user authentication model based on the built-in inertial measurement unit (IMU) of earable devices is proposed. This model extracts user-specific information by collecting vibration signals generated by users performing facial interaction gestures, and achieves diversified implicit continuous user authentication based on intelligent analysis of the above information. To extract accurate and reliable user-specific information, a deep neural network feature encoder based on a Siamese network is proposed, which maps gesture samples of the same user closer in the feature space and enlarges the distance between gesture samples of different users, achieving effective encoding of user-specific information. For continuous user authentication based on user-specific information, a weighted voting strategy based on the distance of the one-class support vector machine hyperplane is proposed, which can adaptively optimize the discrimination boundary to better capture the contained features and structures. The confidence level of the sample is determined based on the distance of the sample points inside and outside the hyperplane, and a weighted voting is designed for authentication. Experimental results show that the method in this paper achieves an authentication accuracy of 97.33% in a single vote, and achieves an authentication accuracy of 99.993% after seven rounds of continuous authentication, which is better than all the methods compared in this paper. It provides a smoother user experience and a higher level of security without the need for passwords, and has a high practical application value.
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图 7 不同数量训练样本的用户分类准确率
Figure 7. User classification accuracy of different number of training samples
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图 9 不同投票轮次的连续认证的假阳率、假阴率、等错率
Figure 9. FNR, FPR, EER values for continuous authentication in different voting rounds
表 1 相关文献所采用的方法
Table 1 Methods Used in the Related References
方法 传感器 生物信号 主要算法 连续认证 设备成本 文献[12] 电容传感 手势 逻辑电路 高 文献[13] IMU 手势 DNN √ 低 文献[14] IMU 手势 域对抗 √ 低 文献[15] IMU 动作 机器学习 低 文献[16] IMU 动作 RNN 低 文献[17] IMU 头部震动 CNN √ 低 文献[18] IMU 下颌震动 CNN √ 低 文献[19] 麦克风 声波 机器学习 低 文献[20] 麦克风 牙齿声纹 DNN 低 文献[21] 麦克风 手势震动 孪生网络 √ 低 文献[22] 麦克风 耳内外声 线性回归 √ 低 文献[23] PPG 心脏活动 SVM √ 高 文献[24] PPG 声波 残差门控网 高 本文 IMU 手势 机器学习 √ 低 
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表 2 20名志愿者的基本信息
Table 2 Basic Information of the 20 Volunteers
身高/cm 体重/kg 年龄 职业 性别 160～170 170～180 180～190 50～60 60～70 70～80 18～22 22～25 25～28 在校学生 教职工 校外人士 男 女 6人 12人 2人 4人 14人 2人 15人 4人 1人 19人 1人 0人 12人 8人
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表 3 数据集格式
Table 3 Data Set Format
序号 加速度计x轴 加速度计y轴 加速度计z轴 陀螺仪x轴 陀螺仪y轴 陀螺仪z轴 标签 手势1 0.964 … 0.972 −0.284 … −0.265 0.053 … 0.057 −0.8 … −4.9 0.1 … 0.7 −0.4 … −0.1 0 手势2 0.968 … 0.980 −0.261 … −0.257 0.022 … 0.068 −1.6 … −2.4 −1 … 0.3 −0.5 … −0.3 1 ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ 手势n 0.976 … 0.984 −0.261 … −0.265 0.034 … 0.04 −0.8 … 2.2 0.4 … −1.1 −0.1 … −0.1 19
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表 4 在智能手机上的部署测试
Table 4 Deployment Testing on Smartphones
系统模块 耗电量/mJ 内存消耗/MB 推理时间/ms 特征编码器 1.06 42.1 32 用户判别器 0.24 12.5 12 总体 1.30 54.6 44
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