EarGate

Ferlini, Andrea; Ma, Dong; Harle, Robert; Mascolo, Cecilia

doi:10.1145/3447993.3483240

Cited by 55 publications

(6 citation statements)

References 27 publications

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“…[97] Kafle  . [98] BreathPrint [101] DolphinAttack [102] PinDrop [109] TeethPass [110] ToothSonic [111] CommanderSong [103] EarEcho [104] LipPass [105] SilentSign [106] EarGate [107] Proximity Echo [108] Apnoea-Pi [92] Fig. 4.…”

Section: Orthogonal Frequency Division Multiplexing (Ofdm)mentioning

confidence: 99%

“…In addition, a deep convolutional neural network (CNN) model with a distance matrix is designed to verify the signature. SilentSign [106] utilizes the speaker in the smart device to emit sound, and the microphone receives the reflected frequencyshifted sound waves to measure the distance change in the pen tip during signing. EarGate [107] observes gait-based recognition from walking-induced sounds, utilizes an in-ear microphone to detect the user's gait from within the ear canal through the occlusion effect of the headset, and achieves a balanced accuracy (BAC) of 97.26%.…”

Section: User Authenticationmentioning

confidence: 99%

“…Regarding the protection problem of voice assistants, the research mainly focuses in two directions: attack detection by using deception to attack its own acoustic defects and the difference in living body information between humans and speakers. Acoustic flaws BreathPrint [101] 2017 Passive ACC: 94% Sniffing, normal breathing and deep breathing; extracting the acoustic feature GFCC as a voiceprint distinction EchoPrint [168] 2018 Active ACC: 98.05% Acoustic face echo & visual face recognition BiLock [169] 2018 Passive FRR: 5%; FAR: 0.95% Teeth bite LipPass [105] 2018 Active ACC: 93.1% Lip reading EarEcho [104] 2019 Passive ACC: 97.57% Ear canal echo AcousticID [170] 2019 Active ACC: 96.6% Gait-based acoustic signal ASSV [171] 2019 Active AUC: 98.7%; EER: 5.5% Phase emits an acoustic signal to estimate phase-dependent changes caused by tiny movements, DCT frequency-domain signatures SilentSign [106] 2020 Active AUC: 98.2%; EER: 1.25% Phase sends an acoustic signal to measure changes in pen tip distance EarGate [107] 2021 Passive FAR: 3.23%; EER: 2.25% In-ear microphone detects gait from the ear canal Proximity-Echo [108] 2021 Active EER: 4.3% Two-factor authentication (2FA): the two devices emit a beep signal alternately and the location features are derived through the microphone inductive echoes, and the extracted signatures are compared TeethPass [110] 2022 Passive ACC: 96.8% Use the sound wave effect produced by the tooth structure ToothSonic [111] 2022 Passive ACC: 95%…”

Section: Voice Assistant Protectionmentioning

confidence: 99%

“…After obtaining the WiFi or acoustic signal, the next step is to characterize it using various sensing techniques. Similar to WiFi sensing, typical applications based on acoustic sensing include daily actions monitoring [73][74][75][76][77][78] , gesture and hand movements recognition [79][80][81][82][83][84][85][86][87] , health caring [88][89][90][91][92] , localization and navigation [93][94][95][96][97][98] and privacy and security [99][100][101][102][103][104][105][106][107][108][109][110][111] . In the following, we will also introduce the basic content of signals and other characterization methods.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Ubiquitous WiFi and Acoustic Sensing: Principles, Technologies, and Applications

Huang

Wang

Zou

et al. 2023

J. Comput. Sci. Technol.

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With the increasing pervasiveness of mobile devices such as smartphones, smart TVs, and wearables, smart sensing, transforming the physical world into digital information based on various sensing medias, has drawn researchers' great attention. Among different sensing medias, WiFi and acoustic signals stand out due to their ubiquity and zero hardware cost. Based on different basic principles, researchers have proposed different technologies for sensing applications with WiFi and acoustic signals covering human activity recognition, motion tracking, indoor localization, health monitoring, and the like. To enable readers to get a comprehensive understanding of ubiquitous wireless sensing, we conduct a survey of existing work to introduce their underlying principles, proposed technologies, and practical applications. Besides we also discuss some open issues of this research area. Our survey reals that as a promising research direction, WiFi and acoustic sensing technologies can bring about fancy applications, but still have limitations in hardware restriction, robustness, and applicability.

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Section: Orthogonal Frequency Division Multiplexing (Ofdm)mentioning

confidence: 99%

Section: User Authenticationmentioning

confidence: 99%

Section: Voice Assistant Protectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Ubiquitous WiFi and Acoustic Sensing: Principles, Technologies, and Applications

Huang

Wang

Zou

et al. 2023

J. Comput. Sci. Technol.

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show abstract

“…Biometric technology is a technology that uses the inherent physiological or behavioral characteristics of a person for identity authentication. In particular, gait is an emerging behavioral recognition feature that is unique between individuals and is difficult to attack through replication techniques [25][26][27]. Compared to biometric features such as face and iris, gait has characteristics such as infallibility and high applicability.…”

Section: Biometric Authenticationmentioning

confidence: 99%

Blockchain-Based Multistage Continuous Authentication for Smart Devices

Yao,

Zhang,

et al. 2023

Applied Sciences

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With the increasing connectivity between multiple smart devices in the Internet of Vehicles, privacy and security have become stringent threats due to unauthorized access. To overcome this issue, designing continuous authentication systems has become an important research topic because of the advantages of continuous monitoring of users after the initial access to the smart devices. Unfortunately, the existing systems are based on a third-party centralized structure, and most of them suffer storage pressure on equipment, thus resulting in significant security hazards and limited performance. In this paper, we propose a multistage continuous authentication system based on blockchain technology and the IPFS, which achieves decentralization and reduces storage pressure. In the first stage of authentication, we adopt Hyperledger Fabric to implement the underlying technical architecture of the blockchain to enhance the security and reliability of identity parameters. The preoutputs of the first-stage authentication are compared against behavioral biometric characteristics stored in the IPFS that aim to accomplish the final authentication. In particular, we use fuzzy extractors to deal with behavioral biometric feature templates, thus solving the privacy problem caused by user information leakage. To evaluate the security of our system, we prove the correctness of the communication protocol and two-way authentication of the scheme using BAN Logic. Furthermore, we use Hyperledger Caliper to analyze the impact of the sending rate of authentication requests on various performance parameters such as throughput, memory, and CPU utilization of the authentication system. Security and experimental results show that: (i) We solve the problem of centralized authentication and can resist replay attacks. (ii) Our scheme can maintain high throughput and effectively reach consensus. Compared to related works, the throughput is improved by 8.6%.

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