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1. A New Deep Anomaly Detection-based Method for User Authentication Using Multi-Channel Surface EMG Signals of Hand Gestures

   https://doi.org/10.1109/TIM.2022.3164162  

   Li, Qingqing; Luo, Zhirui; Zheng, Jun (April 2022, IEEE Transactions on Instrumentation and Measurement)

   User authentication plays an important role in securing systems and devices by preventing unauthorized accesses. Although surface Electromyogram (sEMG) has been widely applied for human machine interface (HMI) applications, it has only seen a very limited use for user authentication. In this paper, we investigate the use of multi-channel sEMG signals of hand gestures for user authentication. We propose a new deep anomaly detection-based user authentication method which employs sEMG images generated from multi-channel sEMG signals. The deep anomaly detection model classifies the user performing the hand gesture as client or imposter by using sEMG images as the input. Different sEMG image generation methods are studied in this paper. The performance of the proposed method is evaluated with a high-density hand gesture sEMG (HD-sEMG) dataset and a sparse-density hand gesture sEMG (SD-sEMG) dataset under three authentication test scenarios. Among the sEMG image generation methods, root mean square (RMS) map achieves significantly better performance than others. The proposed method with RMS map also greatly outperforms the reference method, especially when using SD-sEMG signals. The results demonstrate the validity of the proposed method with RMS map for user authentication. 

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2. Enhancing the Security of Pattern Unlock with Surface EMG-Based Biometrics

   https://doi.org/10.3390/app10020541  

   Li, Qingqing; Dong, Penghui; Zheng, Jun (January 2020, Applied Sciences)

   Pattern unlock is a popular screen unlock scheme that protects the sensitive data and information stored in mobile devices from unauthorized access. However, it is also susceptible to various attacks, including guessing attacks, shoulder surfing attacks, smudge attacks, and side-channel attacks, which can achieve a high success rate in breaking the patterns. In this paper, we propose a new two-factor screen unlock scheme that incorporates surface electromyography (sEMG)-based biometrics with patterns for user authentication. sEMG signals are unique biometric traits suitable for person identification, which can greatly improve the security of pattern unlock. During a screen unlock session, sEMG signals are recorded when the user draws the pattern on the device screen. Time-domain features extracted from the recorded sEMG signals are then used as the input of a one-class classifier to identify the user is legitimate or not. We conducted an experiment involving 10 subjects to test the effectiveness of the proposed scheme. It is shown that the adopted time-domain sEMG features and one-class classifiers achieve good authentication performance in terms of the F 1 score and Half of Total Error Rate (HTER). The results demonstrate that the proposed scheme is a promising solution to enhance the security of pattern unlock. 

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3. “I Can See Your Password”: A Case Study About Cybersecurity Risks in Mid-Air Interactions of Mixed Reality-Based Smart Manufacturing Applications

   https://doi.org/10.1115/1.4062658  

   Yang, Wenhao; Dengxiong, Xiwen; Wang, Xueting; Hu, Yidan; Zhang, Yunbo (March 2024, Journal of Computing and Information Science in Engineering)

   Abstract This paper aims to present a potential cybersecurity risk existing in mixed reality (MR)-based smart manufacturing applications that decipher digital passwords through a single RGB camera to capture the user’s mid-air gestures. We first created a test bed, which is an MR-based smart factory management system consisting of mid-air gesture-based user interfaces (UIs) on a video see-through MR head-mounted display. To interact with UIs and input information, the user’s hand movements and gestures are tracked by the MR system. We setup the experiment to be the estimation of the password input by users through mid-air hand gestures on a virtual numeric keypad. To achieve this goal, we developed a lightweight machine learning-based hand position tracking and gesture recognition method. This method takes either video streaming or recorded video clips (taken by a single RGB camera in front of the user) as input, where the videos record the users’ hand movements and gestures but not the virtual UIs. With the assumption of the known size, position, and layout of the keypad, the machine learning method estimates the password through hand gesture recognition and finger position detection. The evaluation result indicates the effectiveness of the proposed method, with a high accuracy of 97.03%, 94.06%, and 83.83% for 2-digit, 4-digit, and 6-digit passwords, respectively, using real-time video streaming as input with known length condition. Under the unknown length condition, the proposed method reaches 85.50%, 76.15%, and 77.89% accuracy for 2-digit, 4-digit, and 6-digit passwords, respectively. 

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4. Privacy-Preserving Motor Intent Classification via Feature Disentanglement

   Jiahao Fan, Xiaogang Hu (April 2023, 11th International IEEE EMBS Conference on Neural Engineering)

   Recent studies have revealed that sensitive and private attributes could be decoded from sEMG signals, which incurs significant privacy threats to the users of sEMG applications. Most researches so far focus on improving the accuracy and reliability of sEMG models, but much less attention has been paid to their privacy. To fill this gap, this paper implemented and evaluated a framework to optimize the sEMG-based data-sharing mechanism. Our primary goal is to remove sensitive attributes in the sEMG features before sharing them with primary tasks while maintaining the data utility. We disentangled the identity-invariant task-relevant representations from original sEMG features. We shared it with the downstream pattern recognition tasks to reduce the chance of sensitive attributes being inferred by potential attackers. The proposed method was evaluated on data from twenty subjects, with training and testing data acquired 3-25 days apart. Experimental results show that the disentangled representations significantly lower the success rate of identity inference attacks compared to the original feature and its sparse representations generated by the state-of-the-art feature projection methods. Furthermore, the utility of the disentangled representation is also evaluated in hand gesture recognition tasks, showing superior performance over other methods. This work shows that disentangled representations of sEMG signals are a promising solution for privacy-reserving applications. 

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5. Station: Gesture-Based Authentication for Voice Interfaces

   https://doi.org/10.1109/JIOT.2024.3382721  

   Park, Sungbin; Wang, Xueqiang; Chen, Kai; Lee, Yeonjoon (June 2024, IEEE Internet of Things Journal)

   The popularity of smart home devices has led to an increase in security incidents happening in smart homes. A key measure to avoid such incidents is to authenticate users before they can interact with smart devices. However, current methods often require additional hardware. This article proposes STATION, a gesture-based authentication system, an effective gesture-based authentication method built on top of the voice interfaces already available in these smart home devices, without adding new hardware. STATION uses a gesture processing pipeline that identifies Doppler-existing frames and detects the direction of arrival of Reflection to authenticate users in low SNR environments and at longer distances. Furthermore, regarding the nature of gesture-based authentication, this system also supports detecting user liveness, preventing replay and synthesis attacks from remote attackers. The evaluation of STATION shows high accuracy with a false acceptance rate (FAR) of 0.08% and false rejection rate (FRR) of 3.10% for users within 1.5 m of the device. 

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