Zensei

Sato, Munehiko; Puri, Rohan; Olwal, Alex; Ushigome, Yosuke; Franciszkiewicz, Lukas; Chandra, Deepak; Poupyrev, Ivan; Raskar, Ramesh

doi:10.1145/3025453.3025536

Cited by 25 publications

(2 citation statements)

References 44 publications

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“…(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

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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.

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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

“…In HCI, interaction has been conceptualized in terms of mechanical consequence by antecedent, but also as user experience, tool-use or control [44], all of which make user attention or intention a prerequisite for interaction. Additionally, HCI uses are not limited to interaction and one can encounter other phrases such as "implicit sensing" [38,81], "implicit input" [53,75,93], "implicit interface" [87,93] or a specific outcome of user input, as in "implicit authentication" [13]. The diversity of uses raises the question of whether (or what entity within) interaction provides a useful unit of analysis.…”

Section: The Many Meanings Of Implicitmentioning

confidence: 99%

Explicating "Implicit Interaction"

Serim

2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

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The term implicit interaction is often used to denote interactions that differ from traditional purposeful and attention demanding ways of interacting with computers. However, there is a lack of agreement about the term's precise meaning. This paper develops implicit interaction further as an analytic concept and identifies the methodological challenges related to HCI's particular design orientation. We first review meanings of implicit as unintentional, attentional background, unawareness, unconsciousness and implicature, and compare them in regards to the entity they qualify, the design motivation they emphasize and their constructive validity for what makes good interaction. We then demonstrate how the methodological challenges can be addressed with greater precision by using an updated, intentionality-based definition that specifies an input-effect relationship as the entity of implicit. We conclude by identifying a number of new considerations for design and evaluation, and by reflecting on the concepts of user and system agency in HCI. CCS CONCEPTS • Human-centered computing → HCI theory, concepts and models; User models.

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User Recognition Based on Human Body Impulse Response: A Feasibility Study

et al. 2020

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Human recognition technologies for security systems require high reliability and easy accessibility in the advent of the internet of things (IoT). While several biometric approaches have been studied for user recognition, there are demands for more convenient techniques suitable for the IoT devices. Recently, electrical frequency responses of the human body have been unveiled as one of promising biometric signals, but the pilot studies are inconclusive about the characteristics of human body as a transmission medium for electric signals. This paper provides a multi-domain analysis of human body impulse responses (HBIR) measured at the receiver when customized impulse signals are passed through the human body. We analyzed the impulse responses in the time, frequency, and wavelet domains and extracted representative feature vectors using a proposed accumulated difference metric in each domain. The classification performance was tested using the k-nearest neighbors (KNN) algorithm and the support vector machine (SVM) algorithm on 10-day data acquired from five subjects. The average classification accuracies of the simple classifier KNN for the time, frequency, and wavelet features reached 92.99%, 77.01%, and 94.55%, respectively. In addition, the kernel-based SVM slightly improved the accuracies of three features by 0.58%, 2.34%, and 0.42%, respectively. The result shows potential of the proposed approach for user recognition based on HBIR. INDEX TERMS Biosignal, human body channel, identification, impulse response, user recognition.

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Zensei

Cited by 25 publications

References 44 publications

Robust Performance Metrics for Authentication Systems

Robust Performance Metrics for Authentication Systems

Explicating "Implicit Interaction"

User Recognition Based on Human Body Impulse Response: A Feasibility Study

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