Benefiting from smartphones' powerful computing and sensing capabilities, biometric authentication is widely applied to them for conveniently verifying users' identities. However, most biometric features can be easily acquired or reproduced, making them vulnerable to replay and impersonation attacks. To address this issue, we propose DBreathLock, a non-contact deep breath-based authentication system that utilizes a smartphone emitting inaudible frequency-modulated continuous waves (FMCW)-based sonar signals and synchronously records breath sounds and sonar echoes of chest-abdominal-joint (C-A-joint) movements. Then, we implement a dual-protection barrier to defend against advanced replay attacks (ARAs). First, by analyzing the energy features of C-A-joint movements, we develop a Deep Breath Activity Detection method to detect deep breath fragments alongside the capability of resisting ARAs. Second, we take C-A-joint movements and smartphone vibrations caused by holding a smartphone as features and design a liveness detection mechanism to further fortify the resistance to ARAs. Furthermore, a multi-stream identity authentication model is designed to verify legitimate users by fusing biometric features from C-A-joint movements, deep breath sounds, and correlation sequences of both. Extensive real-world experiments with 40 users demonstrate DBreathLock's authentication accuracy of 95.97%. Additionally, it successfully defends against advanced replay, impersonation, simple hybrid, and advanced hybrid attacks, achieving the AUC of 0.9792, and FPRs of 2.17%, 2%, and 4.17%, respectively.

Benefiting from smartphones’ powerful computing and sensing capabilities, biometric authentication is widely used on them to conveniently verify user’s identity. However, most biometric features can be easily acquired or reproduced, making them vulnerable to replay and impersonation attacks. To address these issues, we propose DBreathLock, a non-contact identity authentication system that leverages a smartphone to capture unique biometric features from users’ deep breaths. Specifically, the smartphone emits inaudible frequency-modulated continuous waves (FMCW)-based sonar signals to capture chest-abdominal-joint (C-A-joint) movements, increasing the difficulty of impersonating legitimate users. By analyzing the energy features of CA-joint movements, we develop a Deep Breath Activity Detection (DBAD) method to detect deep breath fragments alongside the capability of resisting advanced replay attacks. To further fortify the resistance to advanced replay attacks, we employ the morphological features of C-A-joint movements and SVC model to recognize these attacks. Following this, we construct mutual information (MI) sequences via calculating the correlation between C-A-joint movements and breath sounds to reduce identity authentication failure caused by physiological changes. Then, a multi-stream identity authentication model is designed to verify legitimate users by fusing the features from C-A-joint movements, deep breath sounds, and MI sequences. Extensive real-world experiments involving 20 users demonstrate that DBreathLock achieves an authentication accuracy of 98.33%. Additionally, it successfully defends against both advanced replay and impersonation attacks.

Currently, powerful and ubiquitous mobile devices provide an opportunity to map physical conditions to cyberspace and realize Digital Twins enabled Healthcare (DTeH). Especially, the impact of the COVID-19 epidemic renders it necessary to keep an eye on the changing trend of respiration. Long-term respiration monitoring helps to assess personal health status and thus becomes an important issue in DTeH. However, previous mobile device-assistant methods mostly implement the monitoring via short-time detection in a best-effort way and with less consideration of identity recognition, the only mean to bind physical vital signs into personal profiles in digital twins space. Thus, it is necessary to introduce the identification to complete string multiple short-time detections and form long-term personal monitoring. To this end, we propose IDRes, an identity-based respiration monitoring system for DTeH. This system employs mobile devices to generate a high-frequency sonar signal to complete respiration detection and identity recognition. As well as it also estimates the respiration rate by tracking the phase change of the sonar signal and recognizes identity via the Doppler frequency shift of the signal to capture characteristics of chest movement. Moreover, via band-pass filtering to remove the low-frequency voice component of the received signals, the usage of the high-frequency sonar signal also enhances security at the physical level. At last, we conduct a series of experiments under different conditions. Experimental results illustrate that IDRes achieves the mean detection error of 0.49bpm with over 93.3% recognition accuracy, and manifest that IDRes can satisfy the requirements of mapping the accurate vital sign data to the personal profile of DTeH.