SilentKey

Tan, Jiayao; Wang, Xiaoliang; Nguyen, Cam-Tu; Shi, Yu

doi:10.1145/3191768

Cited by 44 publications

(9 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(3) The paper had to propose an authentication scheme. Specifically, the paper had to use machine learning to label [12], [33], [41], [42] IMWUT/UbiComp [11], [20], [25], [35], [48] INFOCOM [8], [36], [45], [51], [53] MobiCom [15], [32] MobiSys [7], [31] NDSS [5], [17], [49], [50] Pattern Recognition [2], [9], [18], [24], [38], [40], [54]…”

Section: Review Of Recent Authentication Systemsmentioning

confidence: 99%

Robust Performance Metrics for Authentication Systems

Sugrim

Liu

McLean³

et al. 2019

Proceedings 2019 Network and Distributed System Security Symposium

View full text Add to dashboard Cite

Research has produced many types of authentication systems that use machine learning. However, there is no consistent approach for reporting performance metrics and the reported metrics are inadequate. In this work, we show that several of the common metrics used for reporting performance, such as maximum accuracy (ACC), equal error rate (EER) and area under the ROC curve (AUROC), are inherently flawed. These common metrics hide the details of the inherent tradeoffs a system must make when implemented. Our findings show that current metrics give no insight into how system performance degrades outside the ideal conditions in which they were designed. We argue that adequate performance reporting must be provided to enable meaningful evaluation and that current, commonly used approaches fail in this regard. We present the unnormalized frequency count of scores (FCS) to demonstrate the mathematical underpinnings that lead to these failures and show how they can be avoided. The FCS can be used to augment the performance reporting to enable comparison across systems in a visual way. When reported with the Receiver Operating Characteristics curve (ROC), these two metrics provide a solution to the limitations of currently reported metrics. Finally, we show how to use the FCS and ROC metrics to evaluate and compare different authentication systems.

show abstract

Section: Review Of Recent Authentication Systemsmentioning

confidence: 99%

Robust Performance Metrics for Authentication Systems

Sugrim

Liu

McLean³

et al. 2019

Proceedings 2019 Network and Distributed System Security Symposium

View full text Add to dashboard Cite

show abstract

“…The proposed approach was compared with state-of-the-art approaches developed using the same settings of a single utterance: LipPass [ 23 ], SilentKey [ 24 ], and VocalLock [ 20 ]. The articulation features, approach, and performance of the state-of-the-art and proposed approaches to articulation-based schemes have similar accuracy rates, as shown in Table 2 .…”

Section: Resultsmentioning

confidence: 99%

“…However, their custom device is not well suited for ubiquitous use in mobile computing. Lippass and SilentKey recognized lip patterns using mobile phone ultrasonic signals [ 23 , 24 ]. However, they only recognize long passphrases in the vocabulary pattern because the training data requires a large amount of speech data in a practical scenario.…”

Section: Literature Reviewsmentioning

confidence: 99%

ArtiLock: Smartphone User Identification Based on Physiological and Behavioral Features of Monosyllable Articulation

Wong

Huang

Chen

et al. 2023

Sensors

View full text Add to dashboard Cite

Although voice authentication is generally secure, voiceprint-based authentication methods have the drawback of being affected by environmental noise, long passphrases, and large registered samples. Therefore, we present a breakthrough idea for smartphone user authentication by analyzing articulation and integrating the physiology and behavior of the vocal tract, tongue position, and lip movement to expose the uniqueness of individuals while making utterances. The key idea is to leverage the smartphone speaker and microphone to simultaneously transmit and receive speech and ultrasonic signals, construct identity-related features, and determine whether a single utterance is a legitimate user or an attacker. Physiological authentication methods prevent other users from copying or reproducing passwords. Compared to other types of behavioral authentication, the system is more accurately able to recognize the user’s identity and adapt accordingly to environmental variations. The proposed system requires a smaller number of samples because single utterances are utilized, resulting in a user-friendly system that resists mimicry attacks with an average accuracy of 99% and an equal error rate of 0.5% under the three different surroundings.

show abstract

“…By comparing our proposed silent lip password with recent lip models [48], [49], and [50], our proposed lip model presents better performance. The authors of [48] proposed a silent key using ultrasonic with pre-stored secret words. The accuracy varied between 70% and 83.1% with multiple training times.…”

Section: Related Workmentioning

confidence: 99%

“…The accuracy of authenticating users achieved 90.5% with 40 repetition times. In [48], a silent key system using mouth motions was proposed.…”

Section: Related Workmentioning

confidence: 99%

A Silent Password Recognition Framework Based on Lip Analysis

2020

View full text Add to dashboard Cite

Securing passwords either written or spoken is considered one of the challenging authentication issues faced by individuals and organizations. Written passwords could be easily stolen by look over, or manbehind, while spoken passwords could be recorded and replayed by attackers. A proposed silent password which is based on a dual security model for lip movement analysis will be a promoting solution to these attacks. The goal of the current research is to propose a hybrid voting framework for silent passwords recognition using lip movement analysis. The proposed framework is built for Arabic language by extracting figures from predefined Arabic lexicon. The predefined lexicon mainly contains the Arabic figures from zero to nine with different shapes. The framework takes a video or sequence of images as an input, and outputs the corresponding silent password for the frames extracted from the input video. In this paper, three techniques will be employed to extract effective visual features from mouth-lip movement. Such techniques are SURF, HoG and Haar feature extractor. The resultant features in each technique are fed separately into a classification model, namely, the hidden Markov model (HMM). The HMM identifies corresponding Arabic figure from a predefined lexicon based on input features. The final classification models that are produced from the three techniques have been grouped in a voting scheme to produce the final classification result. The proposed model will be tested on handcrafted data set of lip movement, and it has shown a promising result with improved accuracy of Arabic figures recognition. INDEX TERMS Hidden Markov model, lip analysis, silent password, voting scheme.

show abstract

SilentKey

Cited by 44 publications

References 35 publications

Robust Performance Metrics for Authentication Systems

Robust Performance Metrics for Authentication Systems

ArtiLock: Smartphone User Identification Based on Physiological and Behavioral Features of Monosyllable Articulation

A Silent Password Recognition Framework Based on Lip Analysis

Contact Info

Product

Resources

About